Curriculum for Computer Science Undergraduate Program

  • 23 August 2017, 04.40
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Curriculum 2016

Undergraduate Program in Computer Science UGM 2016 Curriculum is prepared based on Undang-Undang Republik Indonesia Nomor 20 Tahun 2003 tentang Sistem Pendidikan Nasional and Peraturan Menteri Riset, Teknologi, dan Pendidikan Tinggi Republik Indonesia Nomor 44 Tahun 2015 tentang Standar Nasional Pendidikan Tinggi. The curriculum contains a set of plans and arrangements regarding the content and teaching materials and the ways used as guidelines for the implementation of teaching and learning activities prepared in accordance with Keputusan Menteri Pendidikan Nasional Republik Ind noonesia Nomor 232/U/2000 tentang Pedoman Penyusunan Kurikulum Pendidikan Tinggi dan Penilaian Hasil Belajar Mahasiswa and Keputusan Menteri Pendidikan Nasional Republik Indonesia Nomor 045/U/2002 tentang Kurikulum Inti Pendidikan Tinggi.

On 9 February 2018, the Faculty Senate has decided the approval of  the minor change in curriculum for Undergraduate Program in Computer Science. The change has been made for Thesis credits number into 9 credits, and IT Project Management becomes a compulsory subject. This minor change in curriculum will be started in the academic year of 2018/2019.

Click this link to download: Academic Guide Book (before revision) and after revision

List of Courses in Undergraduate Program Curriculum 2016

The compulsory courses consist of subjects that must be taken by each student, which consist of:

  • University Compulsory courses (9 credits), consisting of Pancasila (2 credits), Religion (2 credits), Citizenship (2 credits), and Community Service (3 credits).
  • Faculty compulsory courses (12 credits) consisting of Basic Physics I (3 credits), Basic Chemistry I (3 credits), Calculus I (3 credits), and Programming I (3 credits).
  • Compulsory courses of study program (93 credits).

 

The complete list of courses is presented in Table below

No Course Code Name of course Credit Sem Prerequisite
1 UNU-100X2) Religion 2 1 –
2 MMM-1101 Calculus 1 3 1 –
3 MFF-1011 Basic Physics I 3 1 –
4 MKK-1101 Basic Chemistry I 3 1 –
5 MII-1201 Programming I 3 1 –
6 MII-1202 Programming Lab I 1 1 –
7 MII-1203 Logic for Computer Science 2 1 –
8 MII-1205 Introduction to Computer Science 3 1 –
Subtotal 20
1 UNU-1010 Pancasila 2 2 –
2 MMM-1102 Calculus II 3 2 Calculus I
3 MII-2601 Microprocessor 2 3 –
4 MII-1003 Elementary Linear Algebra 2 2 –
5 MII-1211 Programming II 3 2 Programming I
6 MII-1212 Programming Lab II 1 2 Programming II*
7 MII-1213 Discrete Mathematics 3 2 Informatical Logics
8 MII-1811 Digital Systems 2 2 Informatical Logics
9 MII-1001 English 3 2 –
Subtotal 21
1 UNU-3000 Citizenship 2 3 –
2 MII-2603 Organization and Computer Architecture 3 3 Microprocessor *
3 MII-2501 Database 3 3 Discrete Mathematics
4 MII-2502 Database Lab 1 3 Database*
5 MII-2201 Analysis of Algorithm and Complexity 3 3 Discrete Mathenatics
6 MII-2203 Probability and Stochastic Processes 3 3 Discrete Mathenatics
7 MII-2205 Language and Automaton 3 3 Discrete Mathenatics
8 MII-2207 Computer Graphics 3 3 Calculus II, Elementary Linear Algebra
Subtotal 21
1 MMM-2301 Elementary Differential Equations 3 4 Calculus II*
2 MII-2602 Computer Network 3 4 Discrete Mathenatics
3 MII-2652 Computer Network Lab 1 4 Computer Networks*
4 MII-2611 Operating System 3 4 Computer Organisation and Architecture
5 MII-2612 Operating System Lab 1 4 Operating Systems*
6 MII-2211 Advanced Algorithm 3 4 Analysis of  Algorithm and Complexity
7 MII-2213 Numerical Method 2 4 Elementary Differential Equations*, Elementary Linear Algebra
8 MII-3009 Entrepreneurship and Success Skills 2 4 minimum 50 credits
9 MII-2411 Artificial Intelligence 3 4 Calculus II
Subtotal 21
1 MII-3501 Software Development 3 5 Database, Programming II
2 MII-3502 Software Development Lab 1 5 Software Development*
3 MII-3601 Cryptography and Network Security 3 5 Computer Networks, Discrete Mathenatics
4 MII-3003 Research Method 2 5 minimum 60 fte
Subtotal 9
1 MII-3001 Seminar 1 6 Research Methods
2 MII-3005 Scientific Writing 2 6 minimum 80 credits, Research Methods
3 MII-3007 Philosophy of Computer Science 2 6 minimum 60 credits
4 MII-3411 Machine Learning 3 6 Artificial Intelligence
5 MII-4513 Information Technology Project Management 6 Software Development
Subtotal 11
1 MII-4001 Undergraduate Thesis Proposal 2 7 minimum 100 credits, Seminar*
2 UNU-4500 Community Service Program 3 7 minimum 100 credits
Subtotal 5
1 MII-4004 Undergraduate Thesis 6 8 Undergraduate Thesis Proposal *
Subtotal 6
TOTAL 114

Elective Courses

Elective courses are divided into 2 groups, namely elective courses of Computer Science, and supporting courses. The elective courses of Computer Science are provided by several research laboratories within the Department of Computer Science and Electronics, FMNS, UGM, namely Algorithm and Computational Laboratory (AK), Intelligent System Laboratory (SC), Software Engineering and Data (RPLD), Computer Systems and Networks Laboratory (SKJ), and the Electronic and Instrumentation Laboratory (Elins). For certain reasons or technical consideration, the elective course may change its position from an even semester to an odd semester, and vice versa, or put to sleep (not presented) for a while. The list of Computer Science courses is presented in Table below.

No Course code Course Name Credit Sem Research Lab Prerequisite
1 MII-4201 Distributed Algorithm 3 odd AK Analysis of  Algorithm and Complexity
2 MII-4203 Digital Image Processing 3 odd AK Programming II
3 MII-4205 Checking Model 3 odd AK Discrete Mathematics
4 MII-4207 Management Science 3 odd AK Numerical Methods
5 MII-4211 Compiler Development 3 even AK Language and Automata
6 MII-4213 Queue and Simulation 3 even AK Probability and Stochastic  Process
7 MII-4215 Science Computation 3 even AK Numerical Methods
8 MII-4217 Parallel Programming and Systems 3 even AK Analysis of  Algorithm and Complexity
9 MII-4220 Capita Selecta Algorithm and Computation 3 odd, even AK In accordance with the topic of each capita selecta course, at least 60 credits
10 MII-4401 Bioinformatics 3 odd SC Programming II
11 MII-4403 Artificial Neural Networks 3 odd SC Artificial Intelligence
12 MII-4405 Fuzzy Logic 3 odd SC Artificial Intelligence
13 MII-4411 Pattern Recognition 3 even SC Programming II
14 MII-4413 Expert Systems 3 even SC Artificial Intelligence
15 MII-4415 Decision Support Systems 3 even SC Database
16 MII-4420 Capita Selecta Intelligent Systems 3 odd, even SC In accordance with the topic of each capita selecta course, at least 60 credits
17 MII-4501 Audit and Information Technology Control 3 odd RPLD Software Development*
18 MII-4503 Data Mining and Business Intelligence 3 odd RPLD Machine Learning*
19 MII-4505 Web Programming 3 odd RPLD Programming I
20 MII-4507 E-Business 3 odd RPLD Software Development*
21 MII-4511 Geographic Information Systems 3 even RPLD Database
22 MII-4515 Information Retrieval System 3 even RPLD Database
23 MII-4520 Capita Selecta on Software Engineering and Data 3 odd, even RPLD In accordance with the topic of each capita selecta course, at least 60 credits
24 MII-4601 E-Government 3 odd SKJ Database
25 MII-4603 Digital Forensic 3 odd SKJ Computer Networks
26 MII-4605 Network Analysis and Optimization 3 odd SKJ Computer Networks
27 MII-2606 Network and Mobile Device Programming 3 even SKJ Programming I, Computer Networks*
28 MII-4611 Satellites Development 3 even SKJ Signal and System
29 MII-4613 Telecommunication Network 3 even SKJ Signal and System
30 MII-4620 Capita Selecta on Computer and Network Systems 3 odd, even SKJ In accordance with the topic of each capita selecta course, at least 60 credits
31 MII-1602 Microcontroller 3 even Elins Programming I
32 MII-2215 Signal and Systems 2 even Elins Calculus II*
33 MFF-1012 Basic Physics II 3 even Basic Physics Basic Physics I
34 MII-4621 Capita Selecta on Analysis of Big Data Computational 3 odd RPLD Database
35 MII-4421 Capita Selecta on Genetic Algorithm 3 odd SC Programming I
36 MII-4523 Capita Selecta on Social Informatics 3 even RPLD –
37 MII-4221 Capita Selecta on Computational Logics 3 even AK Calculus II, Discrete Mathematics
38 MII-4521 Capita Selecta on Object Oriented Software Development 3 odd RPLD Software Development*
39 MII-4522 Capita Selecta on Enterprise Systems 3 even RPLD Software Development
40 MII-4030 Industrial Class 3 odd, even – minimum 90 credits

 

 

Islam

Subject:

  1. Human and Religion.
  2. Belief in God Almighty does not go through the process of evolution, but through revelation.
  3. Religious expression.
  4. The points of Islamic teachings.
  5. Human classification according to the Qur'an.
  6. History of the struggle of Muhammad Rasulullah.
  7. Seven groups of people who have the protection of Allah (Hadith).

Reference book:

Ali, M., 1975: Oneness of God In the Qur'an, An Nida.

Catholicism

Subject:

  1. Experience the main points of the Church's teaching and the maturity of the priesthood, for the sake of understanding, the expansion of personal maturation.
  2. Consciousness of the meaning of faith and the internalization of the guidance of the Christian priest, so that by the appreciation of an authentic priest in everyday life as a member of the Church as well as an Indonesian citizen.

Christianity

Subject:

  1. The Meaning of Religion in Human Life;
  2. The Importance of Understanding Christian Doctrine;
  3. Christianity in Indonesia;
  4. Bible and Christian Life; Existence of God and Trinity;
  5. Man Needs Safety; Christian morality and ethics;
  6. Plurality and Pluralism;
  7. Success Becoming a Christian Leader;
  8. Education and Competence;
  9. Ethos Christian Work; Health and Environment;
  10. Christian Faith and Certain Medical Actions;
  11. Christian Faith and Science

Hinduism

Subject:

  1. The concept of God Almighty;
  2. The concept of man;
  3. The concept of the Law of God;
  4. Moral Concepts; Science, Technology and Art; Intercultural harmony;
  5. Community Concepts;
  6. Cultural concepts;
  7. Political concepts;

Reference book:

  1. Dekker, Nyoman and I Ketut Sudari P. Principles of Hinduism.
  2. Pudja, Gede and W. Sadia. Vedic Rig and Sama Vedas. Jakarta: Ministry of Religious Affairs of the Republic of Indonesia. 1979.

Buddhism

Subject:

  1. Preliminary.
  2. Buddha Dharma.
  3. Hinayana / Theravada.
  4. Mahayana.
  5. Tantrayana.
  6. Tripitaka.
  7. Service.
  8. The meanings of parita / mantram, symbol in Buddhism.
  9. Four noble truths.
  10. Eight main roads.
  11. Karma and rebirth.

Reference book:

Soedjas, R. S., 1984: Text Book of Buddhism.

Confucian

Subject:

  1. The foundations of the law of religious life and the subjects of Confucianism.
  2. History arises from its development, its faith and its ethical moral foundations.
  3. Various knowledge about his books, and various things concerning the practice and meaning of worship and ceremony.

Reference book:

SU SI, Sacred Confucian Religion, Matakin

Learning objectives:

  1. Students are capable and proficient in solving problems related to the properties of real numbers, understanding the definition of functions.
  2. Students are capable and proficient in arithmetic limits and derivatives, and can apply it.

Subject:

  1. Set: definition, algebraic operation, properties.
  2. The system of real numbers: properties, inequality, absolute value.
  3. Functions (one variable): understanding, algebraic operations, composition functions, inverse functions. Coordinate system and function graph.
  4. Limit: understanding and properties, unidirectional limits, unlimited limits, natural numbers.
  5. Continuity: understanding and the nature of continuity.
  6. Derivatives: definition, traits, derivatives of compositional functions, inverse function derivatives, derivative function parameters, trigonometric function derivatives, cyclometric functions, hyperbolic functions, exponential functions, logarithmic functions, implicit function derivatives, logarithmic decreases, high-level derivatives . Geometrical / physical definition of the derivative.
  7. Differential
  8. Application of derivatives: maximum / minimum, up / down, convex / concave, stationary point, extreme function and extreme problems in everyday life.
  9. Taylor / Mac Laurin series and its app.


Reference books:

  1. Abe Mizrahi and Michael Sullivan, 1990, Calculus and Analytic Geometry, Wadsworth
  2. James Stewart, 1999, Calculus, 4th edition, Brooks / Cole Pub. Comp.
  3. Robert A. Adam and Christopher Essex, 2010, Calculus, A Complete Course, Pearson.
  4. Tim Pengajar Kalkulus, Diktat Kuliah Kalkulus I, FMIPA UGM.

Subject:

  1. Measurement and Magnitude of Physics
  2. Kinematics
  3. Dynamics I: The Concept of Style
  4. Dynamics II: Business and Energy, Many Particle Systems
  5. Dynamics of Stringent I: Torka and Moments of Inertia
  6. Dynamic Strength II: Equilibrium of Rotation and Translations, Gravity, Fluid, Vibration, Waves
  7. Temperature, Heat and Law of Thermodynamics I,
  8. Entropy and the Law of Thermodynamics II


Reference books:

  1. Halliday, D., Resnick, R and Walker, J., 2014, Fundamental of Physics, Fundamentals of Physics Extended, tenth edition, John Wiley & Sons, Inc., USA.
  2. Tipler, P.A., 2008, Physics for Scientists and Engineers, sixth edition, W. H. Freeman and Company, New York, USA
  3. Raymond A. Serway, and John Jewett, 2014, Physics for Scientists and Engineers, Brooks / Cole Cengage Learning, Singapore.

Subject:

  1. Introduction, Molecules, Ions and Chemical Formulas, Chemical Reactions;
  2. Reactions in solution, Energy changes in chemical reactions;
  3. Atomic Structure, Periodic Table;
  4. Ionic Bond vs. Covalent bonding, Molecular Geometry and Covalent Bonding Model

Reference book:

  1. James E. Brady, Frederick A. Senese, 2009, Chemistry: The Study of Matter and Its Changes 5th edition.
  2. Raymond Chang, Kenneth A. Goldsby, 2012, Chemistry, 11th Edition
  3. Ralph H. Petrucci, William S. Harwood, F. Geoffrey Herring, 2002, General Chemistry: Principles and Modern Applications, 8th ed.

Programming 1 course provides students with knowledge and skills to analyze problems, design algorithms and determine the right data structures so that the computer program generated is structured and efficient. In this course, the programming methodology used is procedural and more focused on algorithm and programming because the data structure used is still relatively simple, starting from basic concept of algorithm, data structure and programming language and how to solve programming problem. With this lecture, students are expected to have the ability to analyze problems, find algorithms and also implement them in computer programs using C ++ programming language.

Subject:

  1. Understanding and components of computer programs, algorithms, data structures and programming languages ​​(1 week).
  2. Stages of problem solving, structured programming concepts and presentation techniques algorithm (1 week).
  3. Simple algorithm on single data, case study of prime checking, determining FPB & KPK and conversion of number system (2 weeks).
  4. Introduction Data Structure and Language Programming C ++, Input / Output Statement Identifier / Identifier, Data Types, Operators (1 week).
  5. Algorithm Structure / Computer Program, Runtunan, control statements Branching (repetition), nestednya (1 week,).
  6. Data type array, Introduction and array declaration, Accessing data on array, Working with many arrays, 2D Matrices / arrays. Data type array (2 weeks).
  7. Data record / struct type, Declaration struct, Access data record / struct (1 week).
  8. Modular Programming / Subprogramming, Definition of subprogram / function, global and local variables, Formal and actual parameters, Recursive Definition, Recursive Subprogram (2 weeks).
  9. Sorting and Searching, Methods of sorting data (isertion sort, selection sort, bubble sort, merge sort, quick sort), data search algorithm (linear search, binary search) (2 weeks).
  10. Data type Pointer, dynamic data structure, pointer declaration, Use of pointer on linked list (1 week).

Reference books:

  1. The C Programming Language 2nd Edition by Brian W. Kernighan, Dennis M. Ritchie, ISBN-13: -0131103627.
  2. Data Structures and Algorithms in C ++, 2001, Second Edition by Adam Drozdek, ISBN 0-534-37597-9.
This lecture discusses some basic concepts of logic. The topics provided include proportional logic, arguments and verification techniques, boolean algebra, predicate logic, and the introduction to capital logic. The concepts will be needed by the students for understanding algorithms and reasoning.
 
Subject:
  1. Propositional logic (definitional propositional logic definition, interpretation, semantic rules, sentence properties, truth tables, semantic trees, falsification, valid sentence schemes, total substitution and partial substitution, double substitution, expanded interpretation, agreement, equivalence ) (2 weeks).
  2. Arguments and verification techniques (definition, valid argument, verification with truth table, inference rule, proving implication, proving biology, proof with natural deduction) (2 weeks).
  3. Boolean algebra (definition, duality principle, laws, addition, multiplication, fundamental product, containment, quantity of product, sum of minimal product, prime implicant, consensus method, canonical form, karnaugh mapping, SAT) 3 weeks).
  4. Predicate logic (definition of sentence in predicate logic, quantifier, sentence quantifiable, variable, constant, predicate, universe of discourse, open sentence, free and bound appearing, open sentence, sentence with double quantification, translation to and from predicate logic) 2 weeks).
  5. Semantics of informal predicate logic (interpretation, substitution, truth quantification, falsity, consistency, equivalence, argument validity by quantification) (1 week).
  6. Semantics of formal predicate logic (extension, interpretation, variable assignment, satisfaction, contradictory, consistency, truth and falsity under interpretations and variable assignments) (1 week).
  7. Advanced predicate logic (valid sentence scheme, validity with additional requirements, equivalence, safe substitution, value property, valid scheme with substitution, recognition and deletion function) (1 week).
  8. Introduction to capital logic (modal logic, linear temporal logic, computation tree logic) (2 weeks).

Reference books:

  1. Bergmann, M, Moor, J., and Nelson, J. The Logic Book. 6th edition. New York, NY: McGraw-Hill, 2014.
  2. Manna, Z. and Waldinger, R. The logical Basis for Computer Programming Vol. 1: Deductive Reasoning, Addison-Wesley Publishing Company, Inc., 1985.
  3. Hughes, G. E., and M. J. Cresswell. A New Introduction to Capital Logic. New York, NY: Routledge, 1996.
  4. Clarke, E.M., Grumberg, O., Peled, D.A., Model Checking. n Edition. The MIT Press, 1999.

This course provides an introduction to computer science as from technology and science. Topics given in this course include how to work computer and program code, information theory, how to work computer hardware, the representation of numbers, the workings of computer software, how the internet work, how digital images and computer science as a science.

Subject:

  1. The nature of computers and code, what they can and can not do, the story of Ada Lovelace's work
  2. Information Theory: Shanon theorem, etc.
  3. How computer hardware works: chips, cpu, memory, disk
  4. Necessary jargon: bits, bytes, megabytes, gigabytes
  5. Number representation, binary representation, floating point
  6. Computer code: loops and logic, How structured data works
  7. How software works: what is a program, what is "running"
  8. How digital images work, Digital media, images, sounds, video, compression
  9. How the internet works: ip address, routing, ethernet, wi-fi
  10. Computer security: viruses, trojans, and passwords
  11. Analog vs. Digital
  12. Big ideas: abstraction, logic, bugs
  13. Computer science as a science


Reference books:

  1. David Reed. A Balanced Introduction of Computer Science. Prentice Hall, 2004.
  2. David R. O'Hallaron. Computer Systems: A Programmer's Perspective, 2 / E. Pearson Publisher, 2010.

Subjects:

  1. The foundation and understanding of Pancasila education
  2. Formulation of Pancasila
  3. Preamble to the 1945 Constitution
  4. Position and function of Pancasila
  5. The shape and structure of Pancasila
  6. The content and meaning of Pancasila, the 1945 Constitution
  7. Implementation of Pancasila.


Reference books:

  1. Notonagoro, 1971, Pancasila Scientifically Popular, CV Pantjuran Tudjuh, Jakarta.
  2. Textbook Composers Faculty of Philosophy, 1990, Pancasila Juridical State, ed.1, Fak. Philosophy UGM.

Learning objectives:

  1. Students are able and proficient in solving problems related to indefinite integrals.
  2. Students can understand a certain integral understanding and its properties.
  3. Students can understand the notion of improper integrals.
  4. Students are able and proficient to use integrals in various applications, such as calculating the area of ​​the flat, the volume of the rotary object, the length of the curve, the area of ​​the rotary, the center of mass, and the moment of inertia.

Subjects:

  1. Indefinite integral: understanding, properties, integrating techniques.
  2. Specific Integral: understanding, properties, Fundamental Theory of Calculus, changing variables. Integral is not fair.
  3. Some examples of specific integral applications: flat area, rotary volume, arc length, swirl area, center of mass / center, Pappus-Guldin Theorem, moment of inertia, Parallel Axis Theorem.

Reference books:

  1. Abe Mizrahi and Michael Sullivan, 1990, Calculus and Analytic Geometry, Wadsworth
  2. James Stewart, 1999, Calculus, 4th edition, Brooks / Cole Pub. Comp.
  3. Robert A. Adam and Christopher Essex, 2010, Calculus, A Complete Course, Pearson.
  4. Teachers Team Calculus, Diktat Kuliah Kalkulus II, FMIPA UGM.

This course focuses on the principles and practice of modern embedded systems design. The discussion will focus on computer architecture outside the CPU, the dasr of the interface hardware / software interface, sensing techniques and real-world controls and other topics. This lecture also introduces microprocessor programming techniques. Although this lecture is not accompanied by a practicum, it is accompanied by programming tasks and the development of an embedded replacement system. The system to be used is ARM Cortex-M3, Actel FPGAs, and some other supporting hardware.

Subject:

  1. Introduction, Architecture (1 Week).
  2. Architecture, Assembly (1 Week).
  3. ISA, Assembly, Toolchains (1 Week).
  4. Memory and I / O Architecture (1 Week).
  5. Memory / Peripheral Bus: AMBAI / O (1 Week).
  6. Memory-Mapped Peripherals (1 Week).
  7. Interrupts, ARM NVIC (1 Week).
  8. Timers (1 Week).
  9. Memory Technologies (1 Week).
  10. Serial busses: UART, SPI, and I2C (1 Week).
  11. ADCs / DACs (1 Week).
  12. Wireless Communications (1 Week).
  13. PCB Design and Fabrication (1 Week).
  14. ARM Cortex-M0 and LPCXpresso (1 Week).

Reference book:

  1. David A. Patterson and John L. Hennessy: Computer Organization and Design: The Hardware / Software Interface, 4th Edition (ARM Edition), Morgan Kaufmann, 2008. ISBN 0123744938, 978-0123744937.
  2. Jean-Loup Baer: Distributed Algorithms: Microprocessor Architecture: From Simple Pipelines to Chip Multiprocessors, 1st Edition, Cambridge University Press, 2009. ISBN: 0521769922, 978-0521769921.
  3. Ronald J. Tocci and Frank J. Ambrosio: Microprocessors and Microcomputers: Hardware and Software (6th Edition) 6th Edition, Prentice Hall, 2002. ISBN: 0130609048, 978-0130609045.

Subject:

Systems of linear equations and solutions, Gauss-Jordan Elimination (Elementary Line Operations), matrix and matrix operations, matrix ratios, matrix operation properties; Inverse matrix, elementary matrix and inverse search method matrix; Types of matrices, Determinants: counting determinants using line reduction, Determinant Properties, Cofactor Expansion, Cramer Rules. Vectors in the Euclid Chamber, vector operation, norm, distance of two vectors, point product, projection, cross product in R3; The linear transformation of the Euclid Space, the properties of linear transformations; Subspaces, linear combinations, linearly independent, linearly independent, vector builders, bases, dimensions, eigenvalues, eigenvectors, characteristic spaces, diagonalizations.

Reference books:

  1. Howard Anton, and Chris Rorres, 2000, Linear Algebra Elementary, Applications Version, Eight Edition, John Wiley and Sons, Inc., New York.
  2. Keith Nicholson, 2001, Elementary Linear Algebra, McGraw-Hill Book Co., Singapore.
  3. Indah Emilia Wijayanti, Sri Wahyuni, Yeni Susanti, 2015, Linear Algebra Basics and Its Use in Various Fields, Gadjah Mada University Press, Yogyakarta.
  4. David C. Lay, 2012, Linear Algebra and Its Applications, 4th Edition Linear Algebra and Its Applications, Addison Wesley. http://web.stanford.edu/class/nbio228-01/handouts/Linear%20Algebra_David%20Lay.pdf
  5. Carl D. Meyer, 2000, Matrix Analysis and Applied Linear Algebra, SIAM http://saba.kntu.ac.ir/eecd/sedghizadeh/Ebooks/Matrix_Analysis.pdf
Programming II Course  is a continuation of the course of Programming I. This course provides knowledge and skills to students to analyze the problems, design algorithms and determine the right data structure for the computer program generated structured and efficient. In the course of Programming 2, more emphasis on data structure, that is discussing various kinds of data structure both linear and non-linear and see the advantages and disadvantages and discuss examples of problems, and also discuss the object-oriented programming (OOP) programming paradigm is a new programming paradigm developed from the facility of derived data types in structured programming. OOP provides an approach in making the design and development of the program more on real-world entity orientation encountered in the real world.

Subject:
 
  1. Introduction: Review Static and dynamic data structure, Abstract data type, linear linked list, Doubly linked list (1 week).
  2. Data Structure Stack: Understanding, implentasi and examples of its use (1 week).
  3. Data structure Queue: Understanding, implentasi and examples of its use (1 week).
  4. Non-linear data structure: Matrix, sparse matrix, multiple linked list (2 weeks)
  5. Data Tree structure: Understanding and terminology, binary search tree, AVL tree and Multiway Trees (2 weeks)
  6. Introduction OOP: Needs development, Java History, OOP case example and OOP basic principle (1 week).
  7. Understanding and implementation of instances in Java: Definition of class, Definition of attribute and method, Definition of instance / object (1 week).
  8. Huffman coding: understanding, text data compression and implementation (1 week).
  9. Graphs: understanding, graph representation, DFS and BFS (2 weeks).
  10. Hashing: Hash table, hash function, Collision resolution and deletion (2 weeks).


Reference books:

  1. Data Structures and Algorithms in Java, 2008, Third Edition by Adam Drozdek, ISBN 0-534-49252-5
  2. The C Programming Language, 2nd Edition by Brian W. Kernighan, Dennis M. Ritchie, ISBN-13: 978-0131103627.

In this course, some basic concepts of discrete mathematics required in the field of computer science are given to students. These basic concepts include mathematical reasoning, discrete structures, algorithmic thinking, and the application and modeling of discrete structures that students will need for understanding algorithms, programming, and data structures.
 
Subject:

  1. Technique of proof (proposition, verification with axiom, counterexample, evidence with contradiction, evidence with case analysis, principle of well-ordering) (1 week).
  2. Set, line, and function (Venn diagram, set operations, cartesian product, power set, cardinality, line, formation of sequences, types of functions, inverse functions, composition, sequence, sum) (1 week) .
  3. Math induction (simple induction, induction steps, strong induction) (1 week).
  4. Introduction to number theory (partition, major alliance factor, fundamental theorem of number theory, modular arithmetic, arithmetic in any modulus, eg number theory application on RSA cryptographic algorithm) (2 weeks).
  5. Graph (introduction to graph theory, non-directional graph, isomorphism, connectedness to graph, staining on graph, planar graph, Hall's Marriage Theorem, tree, tracing tree, spanning tree, directed graph and its properties, acyclic directed graph, topological sorting, Lemma Dilworth) (2 weeks).
  6. Relation and partial order (binary relation, relationship between binary and function relation, equivalence relation, partition, binary relation and directed graph) (1 week).
  7. Summation, multiplication and asymptotics (sum and its solution forms, geometric sequences, geometric sums, infinite geometric sums, integral methods, use of integral methods to find closed-form shapes, double sums, Stirling approaches, and asymptotics notation and its use) (2 weeks).
  8. Recurrence (common form, Towers of Hanoi, formation and completion of recurrence (merge sort), linear recurrence form, divide-and-conquer recurrence form) (2 weeks).
  9. Algebraic system (associative, semi-group, monoid, inverse, group, abelian, subgroup, cyclic group, koset, permutation group, burnside, ring, integral domain, field, finite field) (2 weeks) .


Reference books:

  1. Lehman, E., Leighton, F.T., Meyer, A.R., 2015, Mathematics for Computer Science.
    http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics- for-computer-science-fall-2010 / readings / MIT6_042JF10_notes.pdf
  2. Rosen, K.H., Discrete Mathematics and Its Applications, 7th Edition, McGraw-Hill, 2011.
  3. Judson, T.W., Abstract Algebra: Theory and Applications, 2015th Edition, Orthogonal Publishing L3C, 2015.
    http://abstract.ups.edu/download/aata-20150812.pdf

This lecture presents a discussion of how logical circuits are used to build computers.

Subject:

  1. Boolean algebra, duality, equivalent laws
  2. Combinational logic circuit, Karnaugh map
  3. Sequential sequence logic, flip-flop, latch, register
  4. The finite state machine and synchronization
  5. Memory, counters, and timings
  6. Arithmetic structures
  7. Logic transfer registers
  8. A series of control logic and processor design
  9. Computer system and microcomputer


Reference books:

  1. Digital Logic and Computer Design, Morris Mano
  2. Katz, Randy, and Gaetano Borriello. Contemporary Logic Design. 2nd ed. Upper Saddle River, NJ: Prentice Hall, 2004. ISBN: 9780201308570.
  3. Palnitkar, Samir. Verilog® HDL. 2nd ed. Upper Saddle River, NJ: Prentice Hall, 2003. ISBN: 9780130449115

Subject:

  1. Improving English skills through reading and pronunciation exercises
  2. Fixing grammar
  3. Enrich vocabulary and understand idioms and usage

Organizational study and computer architecture studied the development of organization and computer architecture, including the design of hardware of various models from micro to super computer.

Subject:

  1. Introductory lectures. Organization and architecture of computers, structures and functions. (1 week)
  2. Computer evolution and performance. Know the history and development of computers. Computer architecture design. Von Neumann Architecture. Computer performance. (2 weeks)
  3. Central Processing Unit (CPU). Instructions, operands and operators, addressing. Assembler language. (2 weeks)
  4. The control unit. Operation of the control unit, single bus system. Micro control system. (2 weeks)
  5. Memory management. Cached memory (cache), internal memory, external memory. Virtual memory, allocation, segmentation, paging, maping. (2 weeks)
  6. Input / Output Management. Output input device. Interrupt system, Direct Memory Access (DMA), Interface of the assistive device. The working principle of input device output, keyboard, screen, clever. (2 weeks)
  7. Computer Arithmetic. Unit arithmetic logic (ALU), representation of numbers and arithmetic (integer, floating-point). (2 weeks)
  8. Introduction of parallel computers. pipeline instructions. multiprocessor and supercomputer. The multiprocessor system is closely and loosely coupled. (1 week)

Reference books:

  1. Patterson, DA, Hennessy., JL, Computer Organization and Design, Fifth Edition: The Hardware / Software Interface (The Morgan Kaufmann Series in Computer Architecture and Design), Morgan Kaufmann, 2013. ISBN-10 0124077269, ISBN-13 978-0124077263.
  2. Stallings, W., Computer Organization and Architecture: Designing for Performance, Prentice Hall, 2009, ISBN-10: 0136073735, ISBN-13: 978-0136073734.
  3. Tanenbaum, Structured Computer Organization (6th Edition), Pearson, 2012, ISBN-10: 0132916525, ISBN-13: 978-0132916523

Database is a collection of data in the form of information. The purpose of this lecture is to provide an understanding of the concept of database to students covering the design process and database implementation. Some of the materials that will be discussed in this lecture include basic concept of database, database modeling, relational algebra, SQL language (Structure Query Language) and the latest development of electronic data management
 
Subject:

  1. Introduction DBMS concept (1 week)
  2. Data modeling: Relational data model, distributed data (1 week)
  3. Database design: ER Diagram, the concept of relational data (2 weeks)
  4. Relational Algebra Concepts (3 weeks)
  5. Query languages ​​(2 weeks)
  6. Storage and indexing (2 weeks)
  7. Query processing (1 week)
  8. Transaction processing (1 week)
  9. Recovery (1 week)

Reference books:

  1. Silberschatz, A., Korth, H.F. and Sudarshan, Database System Concepts, 6th Edition, McGraw-Hill, 2010.
  2. Ramakrishnan, R.andGehrke, J., Database Management Systems, 3rd Edition, McGraw-Hill, 2003

In the course of Algorithm and Complexity Analysis, students will be introduced to algorithm theory in general, techniques for analyzing and determining algorithm complexity, as well as providing basic techniques for designing algorithms namely devide and conquer along with algorithmic analysis. Several efficient algorithms were also introduced, accompanied by the analysis and lastly given the theory of complexity.

 Subject:

  1. Analysis of algorithm (1 week).
  2. Asymptotic notations (1 week).
  3. Recurrence (1 week).
  4. Devide and Conquer: maximum and minimum, multiplication of integers, Quicksort. (2 weeks).
  5. Heapsort (1 week).
  6. Sorting with linear time and Order Statistics (1 week).
  7. Probabilistic analysis and random algorithm (1 week).
  8. Amortized Analysis (1 week).
  9. Theory Complexity: how fast we can compute (1 week).
  10. Basic Theory Complexity (including complexity measures-time complexity, P and NP, SAT, poly-time reducibility, probabilistic classes, especially RP and BPP, NP-completeness, Cook-Levin theorem) (2 weeks).
  11. Classical Complexity Theory (including the structure of NP and co-NP, PSPACE, TQBF, Savitch's theorem, Games, Generalized Geography, L and NL, NL = coNL, Oracles, alternating time and space and the polynomial hierarchy, BPP) Sunday).
      

Reference book:

  1. Thomas H Cormen, et.al, Introduction to Algorithms, 2nd Edition. MIT Press / McGraw-Hill, 2001
  2. Dasgupta, Sanjoy, Christos Papadimitriou, and Umesh Vazirani. Algorithms. McGraw-Hill, 2006. ISBN: 9780073523408.
  3. Ingo Wegener. Complexity Theory: Exploring the Limits of Efficient Algorithms, Springer, 2005.

This lecture provides theoretical and applicative foundations for probability and stochastic processes. The course also provides an understanding of mathematical techniques and modeling techniques related to random processes in different fields of application. Topics covered include probability models, discrete and continuous random variables, stochastic processes, Laws of Large Numbers and inference.

Subject:

  1. Probability Models and Axioms Conditioning and Bayes' Rule Independence (1 Week).
  2. Discrete Random Variables: Probability Mass Functions, Expectations, Discrete Random Variable Examples, Joint PMFs (1 Week).
  3. Continuous Random Variables: Multiple Continuous Random Variables, Continuous Bayes' Rule (1 Week).
  4. Derived Distributions; Convolution; Covariance and Correlation (1 Week).
  5. Iterated Expectations; Sum of a Random Number of Random Variables (1 Week).
  6. Bernoulli Process (1 Week).
  7. Poisson Process (2 Weeks).
  8. Markov Chains (2 Weeks).
  9. Weak Law of Large Numbers (1 Week).
  10. Central Limit Theorem (1 Week).
  11. Bayesian Statistical Inference (1 Week).
  12. Classical Inference (1 Week).

Reference books:

  1. Dimitri P. Bertsekas and John N. Tsitsiklis: Introduction to Probability, 2nd Edition, Athena Scientific, 2008. ISBN 188652923X, 978-1886529236.
  2. Athanasios Papoulis and S. Unnikrishna Pillai: Probability, Random Variables and Stochastic Processes, 4th Edition, McGraw-Hill, 2002. ISBN: 0071226613, 978-0071226615.
  3. Sheldon M. Ross: Distributed Algorithms: Introduction to Probability Models, Eleventh Edition, Academic Press, 2013. ISBN: 0124079482, 978-0124079489.

In this course, the basic ideas of computer science theory were introduced to students. The subject matter covers the regular language, which consists of a discussion of finite automata and regular expressions, context-free language, which includes discussion of context free grammars and pushdown automata, Turing machines, as well as computability and connection with Turing machines.
 
Subject:

  1. Grammar, language, automata (strings and languages, some basic terms, basic language operations, grammar, grammar equivalent, Chomsky Hierarchy of Grammars, automata) (1 week).
  2. Finite Automata (description, deterministic finite automata (DFA), Transition Graph, language and DFA, regular language, non-deterministic finite automata (NFA), NFA languages, epsilon-NFA, DFA and NFA equivalents, conversion from NFA to DFA ) (2 weeks).
  3. Regular and regular grammar languages ​​(regular expressions, regular expressions of regular expressions, regular languages, the equivalence of two regular expressions, connections between regular language and regular expressions, regular grammar, right-linear grammar, left-linear grammar, equivalence between regular and regular grammar, regular expression algebra, closure properties of regular languages, closures associated with set operations, closures associated with other operations, identifying non-regular languages, regular lemma pumping for languages) (2 weeks).
  4. Context-free grammars (description, leftmost derivation and rightmost derivation, derivation trees, partial derivation trees, parsing, ambiguity, simple grammar, simplification of CFG, substitution rules) (2 weeks).
  5. Automata Pushdown (description, types, pushdown automata languages, Non-deterministic pushdown automata (NPDA), configuration, NPDA and context-free languages ​​(CFL), CFG for pushdown automata, equivalence between CFG and push-down automata, simplify grammar, deterministic pushdown automata, deterministic CFL, grammar for deterministic CFL, normal forms, normal Chomsky form, normal Graibach form, lemma pumping for CFL) (3 weeks).
  6. Turing machine and computability (Turing engine standard model, Turing machine representation, Turing machine type, how to program, upgrade ability, machine Turing machine, Turing machine, Turing machine as transducers, Church-Turing thesis, Turing machine design, Turing machine models, multitrack turing machines, two-way Turing machines, universal Turing machines, deterministic Turing machines, nondeterministic Turing machines) (2 weeks).
  7. Computability (computability, computability relation with Turing machine, undecidability, recursive language, enumerable recursive language, non-recursive language, non-recursively enumerable language) (2 Weeks).


Reference book:

  1. Hopcroft, J.E., Motwani, R., and Ullman, J.D., Introduction to Automata Theory, Languages, and Computation, 3rd Edition, Addison Wesley, 2006.

This course provides an introduction to computer graphics algorithms, starting from graphic pipeline, primitive graphics, transformation, 2D graphics, 3D graphics, ray casting, ray rendering and ray tracing and animation

Subject

  1. Introduction, graphical system, raster, pixel (1 week)
  2. Graphics Pipeline and Rasterization (1 week)
  3. Primitive graphics: dot, line, polygone, Bezier Curves and Splines (2 weeks)
  4. Curves Properties and Conversion, Surface Representation (1 week)
  5. Coordinates and Transformations, Hierarchical Modeling (1 week)
  6. 2D graphics: transformation, windows to view port, clipping, zooming (1 week)
  7. 3D graphics (1 week)
  8. Color, Shading and Material Appearance, Texture Mapping and Shaders (2 weeks)
  9. Ray Casting and Rendering, Ray Tracing (1 week)
  10. Basics of Computer Animation-Skinning / Enveloping (1 week)
  11. Particle Systems and ODE, Mass Spring Modeling (1 week)
  12. Implicit Integration, Collision Detection and Response (1 week)

Reference books:

  1. Watt, Alan. 3D Computer Graphics. Addison-Wesley, 1999. ISBN: 9780201398557.
  2. Buss, Samuel R. 3D Computer Graphics: A Mathematical Introduction with OpenGL. 2003. ISBN: 9780521821032.
  3. Akenine-Moller, Tomas, Eric Haines and Naty Hoffman. Real-Time Rendering. 3rd ed. A K Peters / CRC Press, 2008. ISBN: 9781568814247.
    4. Shirley, Peter, Michael Ashikhmin, Steve Marschner. Fundamentals of Computer Graphics. 3rd ed. A K Peters / CRC Press, 2009. ISBN: 9781568814698.

Topics:

  1. Introduction to big data
  2. Map Reduce
  3. Data stream processing
  4. In memory processing 
  5. Link analysis 
  6. Locality-sensitive hashing
  7. Large scale machine learning: clustering, nearest neighbour, gradient descent, support vector machine, classification, regression
  8. Data visualization 

References:

  1. Leskovec, J., Rajaraman, A., Ullman, J.D., 2014, Mining Massive Datasets, Cambridge University Press.

Course Computer network is a basic course in the field of digital data communication, especially data communication between computers. After completing this course, students become aware of the benefits and role of computer network in the development of information technology, have scientific competence in the field of Computer Network and have a vision of the development of a broader self.

Subject:

  1. Introductory lectures. The basics of communication and data communication. Data communication components. (1 week)
  2. Computer network and Internet. Computer Networking Models. (1 week)
  3. Application layer. Network application protocols and principles. HTTP, Email, Domain Naming System (DNS) (2 weeks)
  4. Layer Presentation and session layer. Three-way-handshake concept. (1 week)
  5. Transport Layer. Reliable and unreliable network (1 week)
  6. Network applications, socket programming (1 week)
  7. Network Layer, Internet Protocol (IP), unicast, anycast, broadcast, routing, subneting. (2 weeks)
  8. Data Link Layer, logical network topology, conflict management / collision. (2 weeks)
  9. Physical layer, fission network topology, network device, ethernet. (1 week)
  10. Wireless, mobile and multimedia (1 week)
  11. Network management. SNMP, network management model (1 week)


Reference books:

  1. Kurose, J.F., Ross, K.W., Computer Networking: A Top-Down Approach, 6 / E, Pearson, 2012/2013, ISBN-13: 9780132856201.
  2. Tanenbaum, A.S., Computer Networks (5th Edition), Pearseon, 2010, ISBN-10: 0132126958, ISBN-13: 978-0132126953

This course is the introduction course of the Operating System (OS). Explain how the operating system works and provide facilities and services for programmers so that the program can run on a computer machine, how the service interaction between users with computer machines.

Subject:

  1. Modern operating system, the development of the operating system. operating system development, mobile device operating system, virtualization. (2 weeks)
  2. Process management. Principles of process, process and tread, multi treading, process management and tread. process scheduling, scheduling algorithms, real-time scheduling. (3 weeks)
  3. Concurrency. Concurrency problems of processes and treads, synchronization principles and Mutual Exclusion. Producer-consumer issues, deadlock management, starvation. ( 3 weeks).
  4. Memory Management. Memory allocation, segmentation, paging, mapping, memory relocation. Virtual memory. Memory management of various modern operating systems. (2 weeks).
  5. Management input output. output input device, scheduling and interrupting, buffering, cache. (1 week)
  6. File management. Naming system, file system organization, blocking, sharing, permissions, storage management. (2 weeks)
  7. Virtualization (1 week)
  8. Latest operating system trends. Embeded operating system, Android, TinyOS. (1 week)

Reference books:

  1. Stallings. W, Operating System: Internals and Design Principles, Prentice Hall, 2014, ISBN10: 0133805913, ISBN13: 9780133805918
  2. Silberschatz. A, Galvin., P.B., Gagne. G., Operating System Concepts, John Wiley & Sons, 2012, ISBN 9781118063330

Algorithm Advanced Algorithm Course is a continuation of the course of Algorithm Analysis and Complexity. In this course, students will be introduced to some advanced data structures that require more complex analysis and design techniques. In addition, students will also be introduced to algorithms involving graph theory and network flow. Then at the end of this course, students will be given special topics that are advanced and in-depth. These special topics include modern algorithms and mutations that are widely used to solve problems of high complexity.

Subject:

  1. Engineering design algorithms: Dynamic Programming, Greedy Algorithms (2 weeks)
  2. Minimum spanning trees (1 week).
  3. Fast Fourier transform (1 week).
  4. shortest paths (2 weeks).
  5. Network flow (1 week).
  6. Interlude: problem solving (1 week).
  7. van Emde Boas data structure (1 week).
  8. Disjoint-set data structures (1 week).
  9. Sublinear-time algorithms (1 week).
  10. Clustering (1 week).
  11. Derandomization (1 week).
  12. Computational geometry (1 week).
     

Reference books:

  1. Dasgupta, Sanjoy, Christos Papadimitriou, and Umesh Vazirani. Algorithms. McGraw-Hill, 2006. ISBN: 9780073523408.
  2. Kleinberg, Jon, and Eva Tardos. Algorithm Design. Addison-Wesley, 2005. ISBN: 9780321295354.

In this course, various methods for numerical solutions of various mathematical problems are introduced. Topics covered include error, system of linear equations, nonlinear equations, interpolation, differentiation and numerical integration, and differential equations.

Subject:

  1. Error (floating point arithmetic, definition and source error, truncation and round-off, propagation error, stability, convergence) (1 week).
  2. Solving non-linear equations (description of root-search problem, fixed-point method, iterative method, Bisection method, Newton method, Secant method, Muller method, Aitken extrapolation for linear convergent series, Brent algorithm, Newton method for non- linear) (2 weeks).
  3. Numerical solving for systems of linear equations (Gauss elimination, pivoting and scaling on Gaussian elimination, residual correction method, iteration method, numerical solution for Poisson equation, conjugate gradient method, eigenvalue problem, power method, QR method, inverse iteration, squares for linear system) (2 weeks).
  4. Interpolation (Taylor series, definitions, divided differences, Newton interpolation, Lagrange interpolation, errors on polynomial interpolation, Chebyshev interpolation, Hermite interpolation, Spline interpolation) (2 weeks).
  5. Approximations (Weierstrass theorem, Taylor's theorem, least-squares approximations, minimax approximation, near-minimax approximations) (2 weeks).
  6. Numerical differentiation (basic concepts, differentiation with interpolation, Richardson extrapolation) (1 week).
  7. Numerical integration (basic concepts, trapezoidal rules, Simpson rules, Newton-Cotes integration formulas, Gaussian Quadrature, automatic numerical integration, Romberg integration) (2 weeks).
  8. Numerical methods for differential equations (Euler method, multi-step method, midpoint method, trapezoidal method, stiff differential equations and method lines, runge-kutta and single-step method, undetermined coefficients method, boundary value problems) (2 weeks) .

Reference books:

  1. Atkinson, K.E. An Introduction to Numerical Analysis. 2nd edition. John Wiley & Sons, 1989.
  2. Greenbaum A., Chartier, T.P. Numerical Methods: Design, Analysis and Computer Implementation of Algorithms. Princeton University Press, 2012.

The main objectives and targets of this lecture are to make students have or retain the basic techniques and topics of artificial intelligence through lecture explanations, case studies and problem solutions both conceptually and experimentally. The main focus of the lectures on artificial intelligence includes the notion of artificial intelligence, the intelligence approach of intelligent agent agent, the exploration of the issues of knowledge representation and the way of reasoning. various topics, such as heuristic and optimism, logic and probabilistic reasoning, game theory, learning, and perception. Further techniques and approaches of intelligence will be selected and given from areas such as robotics, computer vision, natural language processing, and the philosophy of thought.

Subject

  1. Introduction. Overview of Artificial Intelligence, Example of intelligent system, Intelligence-based agents (1 week)
  2. Troubleshooting. Problem solving with search approach; Search methods such as uninformed search; informed search, A * search, local search, hill climbing, simulated annealing (2 weeks)
  3. Knowledge and reasoning. Formation of knowledge, Inference in first order logic, logical reasoning system (2 weeks)
  4. Acting / acting logically. Principles of planning and acting, Planning in practical (1 week)
  5. Uncertain knowledge and reasoning. Undecs / uncertainly concepts, probability reasoning systems, simple decision making (2 weeks)
  6. Learning. Learning based on observation, neural network learning, belief network, reinforcement learning (2 weeks)
  7. Communication, perception. Natural language processing, translator engine, perception, robotics, communication on intelligent agents (2 weeks)


Reference books:

  1. Norvig & Russell, Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice Hall, Upper Saddle River, N.J., 2010, ISBN-10: 0136042597, ISBN-13: 978-0136042594.

The software is a business product that requires an engineering approach to make it as from the manufacture of a commercial product. This lecture discusses the need for software development methodologies, software development models, principles and modeling of software analysis, software design concepts, data structure design, architectural design, interface design, procedure design and software testing. In addition to conventional approaches, such as waterfall models, prototypes, spirals, also discussed modern software development methods, such as the Angile method, object oriented methods. Management of software development project management will be discussed briefly.

Subject:

  1. Overview of materials and lecture goals. Software as a product; software process model, Types of software, application examples and role of soft software, Software characteristics (1 week).
  2. Software Development Methodology. Software cycles, conventional software development methods, Agile Methods, The Unified Process (UP), Advantages and Disadvantages of software development methods, software development management principles (1 week).
  3. Concepts and principles of analysis. Analysis and requirements specification, Business process and software requirements determination, Principles of analysis, Software specifications (1 week).
  4. Modeling of process and mechanism of analysis. Data modeling, ERD, Functional modeling and information flow, Modeling of system traits / behavior, Structured analysis mechanism (1 week)
  5. Software Design. Principles / design concepts, Modular design (1 week).
  6. Design of system architecture and data. Software architecture, architectural style, database design, Mapping software requirements to architecture (1 week).
  7. User interface design. General interface rules / criteria, User intermediate design, Modeling and task analysis, Phase / interface design activities, examples of implementation (2 weeks)
  8. The basic concept of object-oriented software engineering. Object-based software development approach, Business process and scenario, Object-oriented needs analysis, uses case diagram, class diagram (1 week)
  9. Object oriented software design. UML, Principles of object oriented design, interaction diagrams, examples of Object oriented application programs, Testing object-oriented (2 weeks).
  10. Design on the component level. Graphic design notation, Tabular design notation, Programming language design, Comparison of design notation (1 week).
  11. Software testing techniques. Fundamentals of software testing, Design use test cases, White-box testing, Ground-flow testing, Testing of control structures, Black box testing (1 week).
  12. Strategy of software testing. Strategic approaches to software testing, Unit testing and integrated, Verification and Validation, Testing system, Debugging (1 week).


Reference books:

  1. Ian Sommerville, Software Engineering, 10th Edition, Addison-Wesley, 2016. ISBN-10: 0133943038, ISBN-13: 9780133943030.
  2. Roger S. Pressman, Software Engineering: a Practitioner's Approach, 8th, McGraw-Hill Higher Education, 2014. ISBN-10: 0078022126, ISBN-13: 978-0078022128

Cryptography provides an introduction to the basic principles of cryptography, especially digital krifotgraphy. Symmetric and asymmetric cryptography, public key encryption, digital signature. Network security is given in the form of basic concepts of network security and information. Know the type and type of attack and handling. Application of cryptography in securing data and information.

Subject:

  1. Introductory lectures, information security and communication. Introduction to cryptography, classical algorithms. (1 week)
  2. Hash function, simestris encryption Faistel and non faistel algorithm (2 weeks)
  3. Asymmetric algorithm. RSA, Diffie Hellman, Ellective curve (2 weeks)
  4. Hash and hash algorithms (1 week)
  5. Distribution of encryption keys, public-key encryption. (1 week)
  6. Digital signature. (1 week)
  7. Confidential issues, itegritas and privacy (1 week)
  8. Authentication and system authorization issues and users. (1 week)
  9. Attacks on data and privacy, fabrication, eavesdropping, forgery, viruses, spyware, worms (1 week)
  10. System and network security, system and network attacks, Spam, phishing, botnets, denial of service, firewall, bastian host, DMZ. (1 week)
  11. Basic principles of web security, web application security, content security, session management. (1 week)
  12. Wireless and mobile network security. (1 week)


Reference books:

  1. Kaufman, Network Security (2nd edition), Perlman, and Speciner. ISBN 0130460192.

This course provides an introduction to conceptual and practical research methods for computer science. Lecture materials cover the types of research and research methods used in each step of the research implementation. Such as the formulation of research questions, theoretical assessment, data collection, data analysis, validation, as well as presentations and publications. After following this course, students are expected to understand the main principles of research methods. Students understand how to define research techniques and tools to carry out individual and team research. Students are able to plan, design and conduct research, perform analysis (or interpretation) and build knowledge on computer science. Both theoretical, conceptual, and practical, and consider the ethics and legality.

Subject:

  1. Introduction. Overview and course goals. Understanding and classification of research. Domain area of ​​research in the field of computer science. Types of research and samples (1 week).
  2. Formulation scientifik problem. Selection of theme / research topic. Destination determination, problem coverage, and research questions and examples of proposed solutions (1 week).
  3. Research ethics. Academic integrity; Plagiarism, sanction and prevention of plagiarism; Plagiarism detection software; Legality issues and Patents (2 weeks).
  4. Qualitative and quantitative research methods (1 week)
  5. Software development research method (1 week)
  6. Methods of experimental research (1)
  7. Measurement and data collection. Types of research data; research and measurement variables; Data sampling and sampling techniques; Survey / observation, interview; Data collection software (2 weeks).
  8. Null hypothesis and statistical test. Understanding the null hypothesis; Hypothesis testing; Statistical analysis and interpretation (1 week).
  9. Correlation and regression analysis. Univariate and multivariate data, examples of correlation and regression test data and their interprestation (1 week)
  10. Research management. Multidisciplinary research; Sources of funding and research grants in commuting science and information technology (1 week)


Reference books:

  1. Kothari, C.R., September 1, Research Methodology: Methods and Techniques. New Age International Pvt Ltd Publishers. 2013. ISBN-10: 8122436234; ISBN-13: 978-8122436235.
  2. Creswell, J. W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Second Edition. Sage. 2002.
  3. Wohlin, C., Runeson, P., Höst, M., Ohlsson, MC, Regnell, B., Wesslén, A. Experimentation in Software Engineering, ISBN 978-3-642-29044-2, Springer-Verlag Berlin Heidelberg. 2012.
  4. Final Handbook of FMIPA UGM, 2010, FMIPA UGM, Yogyakarta.

In this course, the students with the guidance of the seminar class supervisor should review some scientific articles. The students then presented it to a seminar class which was attended by lecturers and other seminars students.

Computer Science Philosophy course is basically answer the question of whether the computer can think. Beginning with the concept of philosophy about something (science, history, etc.). Next discusses whether the computer science, followed by discussing whether the computer program. The philosophy of artificial intelligence is the next discussion. This course ends with computer ethics.

Subject:

  1. The meaning of philosophy, and the philosophy of something?
  2. The meaning of science, and computer science
  3. The meaning of computers, computing, and algorithms
  4. Thesis Church and Turing thesis
  5. The meaning of high-level computing (hypercomputation)
  6. Relation of program with model and simulation
  7. The concept of scientific theory of the program
  8. The meaning of computer programs and implementation.
  9. The meaning of the software, copyright (copyright), patents, verification
  10. The meaning of artificial intelligence (Artificial Intelligence)
  11. The relationship of computation and cognition.
  12. Turing Test and Space Argument of China
  13. Do we believe the decision made by the computer? Do we need to build a smart computer

Reference books:

  1. Colburn, Timothy R. (2000), Philosophy and Computer Science (Armonk, NY: M.E. Sharpe); ISBN 1-56324-991-X.
  2. Floridi, Luciano (1999), Philosophy and Computing: An Introduction (London: Routledge); ISBN 0-415-18025-2. Webliography
  3. Floridi, Luciano (2004), The Blackwell Guide to the Philosophy of Computing and Information (Malden, MA: Blackwell); ISBN 0-631-22919-1.
  4. Woodhouse, Mark B. (2003), A Preface to Philosophy, 7th edition (Wadsworth Publishing); ISBN 0534595448.
This course provides an introduction to the concepts, techniques, algorithms in the learning machine, starting from learning theory, guided learning, unstructured learning, classification, linear regression and then the latest topics, including deep learning, support vector machine, hidden markov model and bayesian network.

Subjects :
  1. Introduction, learning theory, supervised learning, unsupervised learning (1 week)
  2. Linear classifiers, separability, perceptron algorithm (single layer perceptron), logistic regression (1 week)
  3. Training objectives, over-fitting, regularization (1 week)
  4. Clustering, k-means, Self Organized Map (1 week)
  5. Non-linear classification, kernels, support vector machine (2 weeks)
  6. Ensembles, boosting (1 week)
  7. Neural networks, multi layer perceptron, backpropagation (1 week)
  8. Deep learning (Auto encoder, CNN, RNN) (2 weeks)
  9. Mixtures and the EM algorithm (1 week)
  10. Representation of probability models: Bayesian networks (1 week)
  11. Hidden Markov Models: modeling, algorithm (2 weeks)


Reference books:

  1. Richard Duda, Peter Hart and David Stork, Pattern Classification, 2nd ed. John Wiley & Sons, 2001.
  2. Tom Mitchell, Machine Learning. McGraw-Hill, 1997.
  3. Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning. Springer, 2009

Project management, especially in the implementation of information technology, becomes a core competency for both entrepreneur and entrepreneur-oriented students. Project management provides the pattern and method of managing project resources by maximizing the available resources and minimizing risk with the project's success objectives. This course is organized to provide students with knowledge, understanding and expertise with the provision of materials that are oriented on PMBook of Knowledge, and best practices of project management. The course also explores students 'ability both hardskills and softkill to provide experience and initiate students' initiatives in managing IT projects in professional ways.

Subjects:

  1. Project Management on IT: definition, step and cycles, problem and facts, tools and certification)
  2. Standard on Project Management: PM Book of knowledge, best practices on Project management, best practices on IT Project management, learning from the world class IT company (Google, Facebook, Microsoft, Apple)
  3. Technical skills on Project Management: Initiation, Plan, Effort & resources allocation, budgeting, Scheduling, milestone, Monitoring & controlling, Risk management, Documentation, Close out
  4. Student project on IT services Project: creative ideas, think out of the box, mentoring and collaboration, delivery & support, release & services
  5. Student project: Presentation & competition

Reference books:

In this course, students prepare their proposals and present them in front of the examiners.

Students do research and compile the report into a thesis. Students present and account for the results of his research on thesis examination in front of the lecturer testers. At the time of the thesis examination, the student must have earned a pass grade for the Thesis Proposal course on the same topic.

Distributed algorithms are algorithms designed to run on multiple processors, without strict centralized control. Generally, such algorithms are much harder to design, harder to understand than single-processor sequential algorithms. Distributed algorithms are important for many practical systems, including local and large-scale computer networks, data management systems, and shared memory multiprocessor systems. Distributed algorithms also have rich theoretical background, and this is the subject of this lecture.

Subjecst:

  1. Course overview. Synchronous networks. Leader election in synchronous ring networks. (1 week).
  2. Leader election in rings. Basic computational tasks in general synchronous networks: leader election. Breadth-first search. Broadcast and convergecast. Shortest paths. (1 week).
  3. Spanning trees. Minimum spanning trees. (1 week).
  4. Fault-tolerant consensus. Link failures: the two generals problem. Process failures (stopping, Byzantine). Algorithms for agreement with stopping and Byzantine failures. Exponential information gathering. (1 week).
  5. Number-of-processor bounds for Byzantine agreement. Weak Byzantine agreement. Time bounds for consensus problems. (1 week).
  6. k-set-agreement. Approximate agreement. Distributed commit. (1 week).
  7. Asynchronous distributed computing. Formal modeling of asynchronous systems using interacting state machines (I / O automata). Proving correctness of distributed algorithms. (1 week).
  8. Non-fault-tolerant algorithms for asynchronous networks. Leader election, breadth-first search, shortest paths, broadcast and convergecast. (1 week).
  9. Synchronizers. Synchronizer applications. Synchronous vs. asynchronous distributed systems. (1 week).
  10. Time, clocks, and the ordering of events. State-machine simulation. Vector timestamps. (1 week).
  11. Stable property detection. Distributed termination. Global snapshots. Deadlock detection. (1 week).
  12. Asynchronous shared-memory systems. The mutual exclusion problem. Mutual exclusion algorithms. (1 week).
  13. More mutual exclusion algorithms. Bounds on shared memory for mutual exclusion. Resource allocation. The Dining Philosophers problem. (1 week).
  14. Shared-memory multiprocessors. Contention, caching, locality. Practical mutual exclusion algorithms. Reading / writing locks. (1 week).

Reference books:

  1. Nancy A. Lynch: Distributed Algorithms, Morgan Kaufmann, 1996. ISBN 0080504701, 9780080504704.
  2. Wan Fokkink: Distributed Algorithms: An Intuitive Approach, MIT Press, 2013. ISBN: 0262026775, 9780262026772.

This course discusses theories and techniques in digital image processing that can be used in various fields such as remote sensing, medical diagnostics, document processing, speech processing and speech recognition and publishing

Subjects:

  1. Image digitation
  2. Image transformation
  3. Coding enhancement
  4. Image restoration
  5. Image compression
  6. Image reconstruction
  7. Image segmentation
  8. Digital image description

Reference books:

  1. Rosenfield, A. and A.C. Kak, 1982, "Digital Picture Processing", Academic Press.
  2. Gonzalez, R.C., Woods, R.E., 2007, "Digital Image Processing", 3rd edition, Pearson.
  3. Pratt, W.K., 2007, "Digital Image Processing", Fourth Edition, John Wiley & Sons.

In this course students are introduced to several techniques for verifying and validating reactive systems. With verification and validation, the correctness of a system or program, both functional and in terms of time and performance, can be determined.

Subjects:

  1. Introduction: reactive system, reactive system modeling, methods for verification and validation, checking model.
  2. Checking model Linear-Time properties: Linear-Time Logic (LTL), regular properties, and checking model.
  3. Model checking with Computation-Tree Logic.
  4. State compression techniques: equivalence, abstraction and partial order reduction.
  5. Model checker for LTL and CTL: SPIN.
  6. Checking model Timed properties, Timed automata, Timed-CTL checking model.
  7. Model checker for TCTL: Uppaal.
  8. Introduction to probabilistic system modeling.


Reference books:

  1. Baier, C., and Katoen, J.-P., Principles of Model Checking, MIT Press, 2008.
  2. Clarke, E.M., Jr., Grumberg, O., Peled, D.A., Model Checking, MIT Press, 1999.
  3. Aceto, L., Ingólfsdóttir, A., Larsen, K.G., and Srba, J., Reactive Systems: Modeling, Specification and Verification, Cambridge University Press, 2007.

Subjects:

  1. The history of management sciences
  2. Linear programming and its solutions
  3. Transportation and assignment problem
  4. Network optimization
  5. Dynamic programming
  6. Integer programming
  7. Non-linear programming
  8. Game theory
  9. Decision analysis
  10. Markov chain
  11. Inventory and queue theories
  12. Forecasting.


Reference books:

  1. Taylor, B.W., 2013, Introduction to Management Science, 11th edition, Pearson.

The course aims to provide students with understanding of the principles of programming language with an emphasis on implementation of programming languages ​​and compiler design, the basics of lexical analysis, parsing and code generation. It is expected that students who attend this lecture have basic knowledge of the compiler and understand the techniques to describe, define and implement the compiler.
 
Subjects:

  1. Intro to Compilers, BNF grammars and parse trees (1 week)
  2. Lexical Analysis (1 week)
  3. Syntax Analysis (1 week)
  4. Top-Down Parsing, Part I (1 week)
  5. Top-Down Parsing, Part II (1 mingg)
  6. Bottom-up Parsing, Part I (1 week)
  7. Bottom-up Parsing, Part II (1 week)
  8. Advanced Parcing (1 week)
  9. Semantic Analysis (1minggu)
  10. Type-Checking (1minggu)
  11. Code Generation (1 week)
  12. Runtime Environment (1 week)
  13. Local and Global Optimization (1 week)
  14. Code Optimization (1 week)


Reference books:

  1. Compilers Construction: Principles and Practice, Kenneth C. Louden, PWS Publishing, 1997, ISBN 0-534-93972-4
  2. Compilers: Principles, Techniques and Tools, by Aho, Sethi, and Ullman
  3. Modern Compiler Implementation in Java (Second Edition) by Andrew Appel, Cambridge, 2002
  4. Basics in Compiler Design by Torben Mogensen

This course aims to equip students about how to analyze the real system, especially about the behavior (behavior) of the real system. Furthermore, students are expected to be able to simulate the real system by making simulation model for evaluation, estimation, determination, experimentation, designing, and analysis ) by applying statistical theories, algorithms, and programming.

Subjects:

  1. Basic Simulation Modeling: System, Model, and Simulation, discrete- event simulation, single server queue server simulation, distributed simulation, simulation steps, other types of simulations.
  2. Modeling of complex systems: processing of lists in simulations, single server queues with Programming Languages, time-shared computer models, multi-teller banks with (or without) jockeying, Job-shop models, and other cases.
  3. Software simulation: comparison with general purpose languages, simulation software classification, desired software features;
  4. Establish valid and reliable simulation models: some valid simulation model principles, verification of simulated computer programs, a three-step approach to development of valid and reliable simulation models, statistical procedures for real-world observation calculations and output data of simulation;
  5. Random-Number Generator: several types of generators, random-number generating tests, random-number generators on microcomputers;
  6. Formation of random variables: common approaches for the formation of random variables, the formation of continuous variables, the formation of random variables.
  7. Simulation of Manufacture Systems (optional): simulation objectives at the factory, simulation software for factory applications, system modeling randomness.

Reference books:

  1. Albrecth, M.C., and Az, P.E., Introduction to Discrete Event Simulation, 2010.
  2. Law, A.M., and Kelton, W.D., Simulation Modeling and Analysis, 2nd Edition, McGraw-Hill, 1991.

This course discusses how to model real-world cases into linear or nonlinear functions with limiting functions or not.

Subjects:

  1. Discretization and stable conditions
  2. Looking for solutions of linear and non linear functions
  3. Finding a solution of a non-linear function with or without a delimiter function
  4. Use of integration, regression, interpolation and extrapolation techniques to find solutions
  5. Finite element
  6. Approach the error value


Reference books:

  1. Numerical Recipes for Fortran90

This course begins with a brief overview, including the concepts and terminology associated with parallel computing systems. The topic of parallel memory architecture and parallel programming model is then explored. This topic is followed by discussion on a number of issues related to the design of parallel programs. This lecture concludes with some examples of how to parallel simple sequential programs.

Subjects:

  1. Introduction: Concepts and terminology of parallel systems, Utilization of parallel systems, Examples of parallel systems
  2. Parallel computer memory architecture: Shared memory, Distributed memory, Hybrid distributed-shared memory
  3. Parallel programming model: shared memory model, thread model, distributed memory / message passing model, parallel data model / CUDA / MapReduce, hybrid model, SPMD (Single Program Multiple Data) and MPMD (Multiple Program Multiple Data)
  4. Parallel program design: Understanding Problem and program, Partitioning, Communication, Synchronization, Data dependency, Load balancing, Granularity, Input / output, Limitations and complexity of parallel program, Performance and tuning analysis
  5. Examples of problems and parallel programs: Array processing, Descriptive statistics (main topics), Image processing, Computing and Big Data

Reference books:

  1. Ananth Grama, Anshul Gupta, George Karypis, Vipin Kumar, 2003, Introduction to Parallel Computing (2nd Edition), Addison Wesley.
  2. Blaise Barney, 2011, Introduction to Parallel Computing, Lawrence Livermore National Laboratory
  3. Blaise Barney, 2011, Message Passing Interface (MPI), Lawrence Livermore National Laboratory
  4. Ian Foster, Designing and Building Parallel Programs, Addison-Wesley.

Course: Fractal Design

In this course, students are introduced with fractal techniques to create 2D or 3D object models.

Subjects:    

  1. Introduction: Introduction to Fractals and Underlying theories.
  2. Transformation in metric space, Contraction Mapping and Fractal Construction
  3. Chaotic dynamics on Fractal and Fractal Dimension
  4. Interpolation and Fractal Compression
  5. Fractal in the real world: Trees, Rivers, Clouds, Snow, Mountain
  6. Fractal Modeling.

Reference books:

  1. Barnsley, M., 1988, "Fractals Everywhere", Academic Press, Inc., New York.    
  2. Finlay, M. and Banton, K., 1993, "Real-World Fractals", M & T Books, New York.
  3. Falconer K., 1990, "Fractal Geometry, Mathematical Foundations and Applications", John Wiley & Son, New York.
  4. Oliver, D., 1992, "Fractal Vision: Put Fractals to Work for You", Sams.

 

Course: Machine Vision

Subjects:

  1. Camera and Geometry Calibration (Camera Calibration and Geometry)
  2. Stereo Corrondence (Stereo Corrondence)
  3. Structure of Motion (SFM)
  4. Estimated Density (Dense Motion Estimation)
  5. Reconstruction of Three Dimensional Objects (3D Object Reconstruction)
  6. Introduction to Augmented Reality (Introduction of AR)

The rapid increase in the amount of biological data currently causes manual data analysis to be no longer efficient. It takes computation to help analyze data so it can extract important information from a large set of genetic data. This region is called bioinformatics. Students are able to understand the concept of molecular biology, understand the techniques used in bioinfromatics, use biological databases, develop bioinformatics tools and write bioinformatics research results in the form of scientific papers.

Subjects:

  1. Introduction of biomolecular concepts
  2. Sequence Macthing Algorithms → dynamic programming, recursive algorithm, divided and conquer algorithm
  3. Pairwise sequence alignment
  4. Multiple sequence alignment → hidden markov model
  5. Searching database
  6. Protein Structure Prediction → machine learning, svm
  7. Genome Informatics -> Genome Assembly


Reference books:

  1. Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G., 1998, Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Cambridge University Press, New York
  2. Jones, N.C., and Pevzner P.A., 2004, An Introduction to Bioinformatics Algorithms, MIT Press, Cambridge
  3. Colton, S., 2007, Introduction to Bioinformatics, Genetics Background, Course 341 Lecture Slide. Department of Computing at Imperial College, London

This course offers a wide and in-depth introduction of neural network (ANN), a new approach to modeling, formulation, and problem solving. Networks of units such as neurons and extensive inter-unit connections have demonstrated excellent performance in applications in areas such as pattern analysis, nonlinear control, combinatorial optimization, and knowledge acquisition where traditional Von Neumann machines and algorithmic approaches can not handle them. Success in practical applications and the rapid progress of theoretical ANN research have aroused great interest among the various disciplines and made it one of the most active areas of research in computer science today.
 
Subjects:

  1. Introduction: Why NN; What is a NN; Where are NNs being used? ; How NN Used ?; Who is developing NN; When NN Began: the McCullock; Suggestion for futher study
  2. Simple Neural Nets for Pattern Classification. : General Architecture; Hebb Net; Perceptron; Adeline
  3. Pattern Association: Heteroassociative memory; Associative Net; Iterative Autoassociative Net; Bidirectional Associative Memory;
  4. Neural Network Based on Competition: Fixed-Weight Competitive Nets; Kohonen Self-Organizing Map; Learning vector Quantitation; Counterpropagation
  5. Adaptive Resonance Theory: Understanding of the patterns, features; Components of a pattern recognition system; The development of pattern recognition systems and applications
  6. Backpropagation (BP) Neural Net: BP Standard; Variation; Theoritical Results;
  7. A Sampler of other neural networks: Fixed weight net for constrained; A few more nets and learn; Adaptive Architecture; Neocognitron


Reference books:

  1. Laurene Fausett, 2004, Fundamentals of Neural Networks: Architecture, Algorithms, and Application
  2. Simon Haykin, Neural Networks a Comprehensive Foundation, Pearson Education

Fuzzy logic, some vague, or blurred, is the development of classical logic which has only two truth values: True / False, True / False, 1/0. The value of the truth of fuzzy logic is expanded into a certain interval, generally [0, 1]. This results in logical operators experiencing drastic changes with different interpretations. Similarly, the interpretation of implications, ponens mode, and so on has undergone enormous changes. Fuzzy logic is very useful in control systems, expert systems, and classification systems.

Subjects :

  1. Introduction: Function and membership of the set;
  2. fuzzy set; the properties of the fuzzy set; operator on fuzzy set:
  3. complement, s-norm, t-norm;
  4. fuzzy relation and fuzzy relation composition; the special properties of fuzzy relations;
  5. linguistics; fuzzy proposition;
  6. fuzzy logic operator;
  7. fuzzy implications (fuzzy rule);
  8. generalized mode ponens; generalized mode tollens; generalized hypothetical syllogism;
  9. fuzzy rule system; fuzzification and defuzzification.

Reference books:
   

  1. Wang, L., 1997, "A Course in Fuzzy Systems and Control", Prentice-Hall International, Inc., New Jersey.
  2. Klir, G.J. and T.A. Folger, 1988, "Fuzzy Sets, Uncertainty, and Information", Prentice-Hall, New Delhi.
  3. Zimmerman, H.J., 1991, "Fuzzy Set Theory and Its Applications", Kluwer Publishing Co., Amsterdam.
  4. Kaufmann, A. and M.M. Gupta, 1991, "Introduction to Fuzzy Arithmetic Theory and Applications", Van Nostrand Reinhold, New York.

This course discusses a variety of methods of pattern recognition, and its implementation in applications.

Subjects:

  1. The structure of the pattern recognition system.The pattern and extraction of its main features (feature).
  2. Introduction of statistical patterns: parametric and non parametric approaches to guided learning, unattended learning.
  3. Syntactic pattern recognition: grammar recognition, graphical approach.
  4. Introduction of artificial neural network pattern (JS): JS-based pattern association, linear associative mapping, JS trace forward with retraction training.
  5. Vague logical-based pattern recognition: faint set, cryptic set and membership function, clustering of vague logic.
  6. Examples for the four pattern recognition groups.

Reference books:

  1. Schalkoff, R, Pattern Recognition: Statistical, Structural and Neural Approaches, John Willey & Sons, 1992.2.
  2. Bezdek, J.C, Pal, S.K; Fuzzy Models for pattern Recognition: Methods that search for Structures in Data, IEEE Press, 1992.

Expert System is a system that uses the knowledge in the computer to solve a problem, which usually can only be done by an expert. This lecture discusses the latest technology in a knowledge-based expert system, expert system development methodology, knowledge acquisition, knowledge representation, inference method, uncertainty method and its implementation for case resolution. The purpose of this course is to provide understanding to students about the concept of expert system development, acquisition process and knowledge representation, inference method and reasoning in the expert system. From this lecture students are expected to also be able to build a prototype expert system to solve a particular case.

Subjects :

  1. The concept of expert system
  2. Expert System Architecture
  3. Knowledge Representation, Inference Machine
  4. Method of reasoning, reasoning with uncertainty
  5. Model and management of uncertainty
  6. Expert system design and application
  7. The development and application of expert systems in various fields