MII 5011 Analysis of Algorithms (3 credits)
Prerequisites: Algorithm and Programming

Course Outcomes:
CO-1: Students be able to apply the solution of System of Linear Equations, series, and recurrence.
CO-2: Students be able to apply a theory about running time of the algorithm.
CO-3: Students be able to explain and calculate algorithm complexity with asymptotic notation.
CO-4: Students be able to apply abstract data type (stack, queue, etc).
CO-5: Students be able to apply divide and conquer methods.
CO-6: Students be able to apply optimization theory and to analyze an algorithm.

Syllabus: Systems of linear equations, graphs, and series; Calculation of the time and space complexity of the algorithm; Notation of complexity (best, average, and worst case); Use of abstract data type to support algorithm optimization (stack, queue, etc.); The concept of Divide and Conquer; Optimization Theory (Dynamic Programming, Greedy Algorithm, Amortized Analysis, Graph Algorithm, etc.) and its analysis.

References:
1. Arora, A., Analysis and Design of Algorithms 2nd Edition, Cognella Academic Publishing, 2014.
2. V. Aho, A., D. Ullman, J., E. Hopcroft, J., Data Structures and Algorithms, Pearson, 1983.
3. Brassad, G., Fundamentals of Algorithmics, Pearson, 1995.

MII 5051 Artificial Intelligence (3 credits)
Prerequisites: Logic.

Course Outcomes:
CO-1: Students be able to explain the definition of intelligent agents, PEAS and PAGE description and able to apply the theory of intelligent agents to solve real problems.
CO-2: Students be able to formulate problem solutions with a search approach and be able to use search methods for completion
CO-3: Students be able to explain the method of knowledge representation of known information and able to apply knowledge representation to real problems.
CO-4: Students be able to explain the mechanism of an inference engine and use it for real problems.
CO-5: Students be able to explain the definition of NLP, NLP components and NLP applications.
CO-6: Students be able to explain the definition of pattern recognition and pattern recognition applications

Syllabus: Agent Definition; Various Agents; Description of PEAS; Description PAGE; Definition of Knowledge; Logic; Rule; Frame; Semantic Network; Scripts; Informed search; Uninformed search; Inference engine. Definition of NLP; NLP Components; NLP Application, Pattern Recognition Definition; Pattern Recognition Components; Pattern Recognition Application.

References:
1. Russell, S. and Novig, P., Artificial Intelligence: A Modern Approach, 3rd Edition, PrenticeHall, 2009.
2. George F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th Edition, Addison-Wesley, 2008.
3. Michael Negnivitsky, Artificial Intelligence: A Guide to Expert Systems, 2nd Edition, Addison Wesley, 2004.
4. W. Firebaugh, Artificial Intelligence: A Knowledge-Based Approach, W. Firebaough PWS-Kent Publishing Compan, 2009.

MII 6001 Research Methodology (3 credits)
Prerequisites: -

Course Outcomes:
CO-1: Students be able to formulate research topics and questions and formulate their research planning.
CO-2: Students be able to choose suitable methods to answer predetermined research questions and contribute to the modification of existing methods.
CO-3: Students be able to write proposals and research reports in the form of publications or other scientific works.

Syllabus: Formulate research questions and plan them; Use of appropriate methods and contribute to new research; Writing research documentation in scientific papers.

References
1. P. Daniel, S., Research Methodology, Kalpaz Publications, 2011.

MII 5031 Software Engineering (3 credits)
Prerequisites: -

Course Outcomes:
CO-1: Students be able to explain the types of software, software development model and software characteristics.
CO-2: Students be able to explain and implement the management of software projects
CO-3: Students be able to explain and apply conventional software design methods, from analysis to testing.
CO-4: Students be able to explain the method of unconventional software development.
CO-5: Students be able to explain and do software design with object approach.
CO-6: Students be able to explain and do the design of software for client/server application, web or mobile.

Syllabus: software and its characteristics, software development models, measures in software projects, software project management (planning, risk management, scheduling, quality assurance), conventional and unconventional software design methods (agile, UP, Scrum), object-oriented design, design of client-server, web and mobile software, introduction of software testing.

References
1. Roger S. Pressman, Software Engineering: a Practitioner's Approach 8th, McGraw-Hill Higher Education, 2014.
2. Sommerville, I., Software Engineering, 10th Edition, Addison-Wesley. ISBN-10: 0133943038, 2016.

MII 6033 Database Management Systems (3 credits)
Prerequisites: Having knowledge about bigdata system

Course Outcomes:
CO-1: Students be able to explain the importance of database in information management.
CO-2: Students be able to explain data models (spreadsheet, relational, object, semi-structured, graph, text, distributed-database) and its characteristics.
CO-3: Students are capable of modelling or formulating real problems into the appropriate database model.
CO-4: Students be able to explain the concept of relational algebra as the basic theory of operation in a relational database
CO-5: Students be able to use query language (SQL, NoSQL, graph-based query, SPARQL).
CO-6: Students be able to implement optimization mechanism in database: indexing, normalization, transaction.
CO-7: Students be able to explain the latest technology related to electronic data management.

Syllabus: Database in data and information management, data models (relational, semiterstructure, graph, text, distributed), procedural query languages (relational algebra), non- procedural query languages (SQL, SPARQL, XQuery, Cypher), functional dependency, closure, normalization, indexing, transaction handling (concurrency, rollback, commit), case studies.

References:
1. Silberschatz, A., Korth, H.F. and Sudarshan, Database System Concepts, 6th Edition, McGraw-Hill, 2010.
2. Robinson, I., Webber, J., & Eifrem, E., Graph databases. O'Reilly Media, Inc., 2013.
3. Ramakrishnan, R.and Gehrke, J., Database Management Systems, 3rd Edition, McGraw- Hill, 2003.
4. Strauch, C, NoSQL Databases, Stuttgard Univ.

MII 6214 Applied Algebra and Statistic (3 credits).
Prasyarat: Relation and Statistics

Course Outcomes:
CO-1: Students be able to explain the theory and apply SPL, descriptive statistics, probability, and inference statistics.
CO-2: Students be able to explain the standard methods for finding solutions of linear equations.
CO-3: Students be capable of applying methods to search for solutions of nonlinear equations with and without limitation.

Syllabus: SPL, Descriptive Statistics, Probability, Statistics Inference; search for solutions with standard methods for linear equations; search for nonlinear equation solutions with and without constraint

References:
1. C. Lay, D., Linear Algebra and Its Applications 5th Edition, Pearson, 2015.
2. Hill, T., Lewicki, P., Statistics: Methods and Applications, StatSoft, 2005.

MII 6838 Big Data Analysis (3 credits)
Prerequisites: Database

Course Outcomes:
CO-1: Students be able to explain the characteristics and definitions of Big Data and know the benefits of Big Data on business strategies and decisions.
CO-2: Students be capable of explaining and implementing Big Data technology for processing large amounts of data and gaining experience with Analytical tools to solve problems in Big Data.
CO-3: Students be able to explain and implement techniques and algorithms to process Big Data using Hadoop and Spark to solve problems
CO-4: Students be able to explain and implement Big Data process preparation and visualization.
CO-5: Students be able to explain and implement Big Data modelling using Machine Learning.

Syllabus: Big Data Concepts, Big Data Benefits and Role in determining business strategy and decisions, Big Data ecosystem architecture, Big Data processing with real-time model and batch processing, Hadoop environment, MapReduce, Spark, Scala. Big Data analysis using libraries in Apache Mahout, Machine Learning. Visualisation Big Data using Django Framework, Saiku and similar visualization tools to display the results of Big Data analysis.

References:
1. Baesens, B., Analytics in a big data world: The essential guide to data science and its applications. John Wiley & Sons, 2014.
2. Marz, N., & Warren, J., Big Data: Principles and best practices of scalable real-time data systems. Manning Publications Co, 2015.

MII 6632 Data Science (3 credits)
Prerequisites: Mathematics for Data Science

Course Outcomes:
CO-1: Students be able to explain the understanding, scope and role of Data Science.
CO-2: Students be able to formulate real-world problems into Data Science problems.
CO-3: Students be able to explain various types of data and able to collect data needed for Data Science purposes.
CO-4: Students be able to explain and apply methods for exploration and data preparation 5.
CO-5: Students be able to do the modelling (supervised and unsupervised) and evaluate the model.
CO-6: Students are capable of implementing the model in an application form (model deployment).
CO-7: Students are capable of performing science data processing using Python or R programming language

Syllabus: The concept of Data Science and its scope, exploratory data analysis, the method of analysis (regression, clustering, classification), learning algorithms (linear regression, k-means, k-NN, Naïve Bayes, association rules), data collection (API, web scraping), data preparation, modelling and evaluation, application development and visualization, application development in Phyton or R.

References:
1. Schutt, R., & O'Neil, C., Doing data science: Straight talk from the frontline." O'Reilly Media, Inc.", 2013.
2. James, G., Witten, D., Hastie, T., & Tibshirani, R., An introduction to statistical learning (Vol. 112). New York: Springer, 2013.
3. Provost, F., & Fawcett, T., Data Science for Business: What you need to know about data mining and data-analytic thinking. " O'Reilly Media, Inc.", 2013.
4. McKinney, W., Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. " O'Reilly Media, Inc.", 2012.

MII 6292 Electronics (3 credits)
Prerequisites: Basic Concepts of Digital Electronics

Course Outcomes:
CO-1: Students be able to explain the basic concept of digital electronics
CO-2: Students be able to explain the development of the latest digital electronics
CO-3: Students be able to analyze digital electronics problem
CO-4: Students be able to apply the principles of digital electronics to solve problems in the form of design
CO-5: Students be able to modify the sub-system of digital electronics

Syllabus: Gate of reasoning, Boole Algebra, Karnaugh Map, Clock, Flip-flop, Counter, register, multiplexer, demultiplexer, operation, timing, interfacing, Architecture Set Instruction, Programming, Modeling, Programable Logic Array, PLA Instruction Set, PLA Program.

References:
1. Comer-Douglas, Essentials of Computer Architecture, 2nd ed, Taylor & Francis, 2017
2. M. M. Mano and C. R. Kime, Logic and Computer Design Fundamentals," 5th ed., Prentice Hall, 2015.
3. Pullock,W., Inside the Machine: An llustrated Introduction to Microprocessor and Computer Architecture, 2nd ed, 2007

MII 6294 Instrumentation (3 credits)
Prerequisites: Understand digital concept and measurement system

Course Outcomes:
CO-1: Students be able to explain basic concepts of instrumentation, measurement, and control
CO-2: Students be able to explain aspects of accuracy and precision in measurement
CO-3: Students be able to explain static and dynamic characteristics of instrumentation system
CO-4: Students be able to explain the type, nature and designation of sensors and actuators
CO-5: Students can design signal signals
CO-6: Students be able to explain the order control system 1 and order 2

Syllabus: Block diagram, sensor, actuator, physical, mechanical, electronic, thermal, fluid, signal conditioning, type of measuring instrument, sensitivity, range measure, steady state, steady state error, transient response, overshoot, settling time, rise time, -type of communication (series, parallel, protocol, sync, out of sync), open-loop, closed-order calendar, closed-order order 2, transfer function.

References:
1. Plancko, D., Fundamentals of Instrumentation and Measurement, Willey & Son, 2007
2. Dunn, W. C., Fundamental of Industrial Instrumentation and Process Control, McGraw Hill, 2005.

MII 6836 Software Quality Assurance (3 credits)
Prerequisites: Software Engineering

Course Outcomes:
CO-1: Students be able to explain the concept, the main principle and importance of software quality management.
CO-2: Students be able to explain the process of quality assurance and its role in software development.
CO-3: Students be able to explain and apply various techniques, methods, and software quality testing tools.
CO-4: Students be able to apply verification and validation techniques for assessment/review process of status/state of software quality.
CO-5: Students be able to explain and apply the ISO 9000 quality model, maturity model and software quality.
CO-6: Students be able to explain principles, components, methods for auditing and quality control of software, information technology within the organization.

Syllabus: The main topics covered from this course are the notions of quality assurance concepts, software architecture, quality models; test methods, software quality testing tools; usability, software quality matrix, measurement and financing cost model; ISO 9001, 9003 quality assurance model, CMMI and CMMI assessment methodology, bootstrap methodology, audit and quality control of software and information technology within the organization.

References:
1. Everett, G. D., & McLeod Jr, R., Software testing: testing across the entire software development lifecycle. John Wiley & Sons, 2007.
2. Tian, J., Software quality engineering: testing, quality assurance, and quantifiable improvement. John Wiley & Sons, 2005. William, P., Effective methods for software testing, Wiley Publishing, Inc, 2006.

MII 6272 Computer Network (3 credits)
Prerequisites: -

Course Outcomes:
CO-1: Students can explain the development of computer network.
CO-2: Students can explain the layers of computer network.
CO-3: Students can explain and apply signal dispersion technology.
CO-4: Students can explain the concept and design the communication protocol.
CO-5: Students can explain the concept of data transfer security.

Syllabus: This lecture presents a computer network from the standpoint of a five-layer internet model. Each layer is dealt with separately to provide a thorough and comprehensive understanding of computer network problems. Computer network architecture and its development, layering in computer network data communication technology, computer network protocol and QoS network, network security

References:
1. Forouzan, B., A., Data communication and Networking, 5th Edition, Mc. Graw Hill, 2012.
2. Andrew S. Tanenbaum, Computer Network 5th Ed., Prentice Hall, 2011
3. James F. Kurose and Keith W. Ross, Computer Networking: A Top-Down Approach 6th Ed., Addison-Wesley, 2013
4. Beasley, J., S., Networking, 2th Edition, Prentice Hall, 2009.
5. Goralski, W., The Illustrated Network, Elsevier, 2009.

MII 6874 Network Security (3 credits)
Prerequisites: Computer Network

Course Outcomes:
CO-1: Students can explain aspects of network security.
CO-2: Students can explain computer network security algorithm/mechanism.
CO-3: Students can design and implement the security of network systems, including the use of WiFi.
CO-4: Students can explain network-related security system layer in a computer network
CO-5: Students can identify various techniques of network computer attack.

Syllabus: This course deals with security aspects of computer networks, security threats and services, encryption algorithms and symmetry/asymmetry keys, authentication services and hash functions, security at TCP / IP network layers and IPSec protocols, email and web application layer security, attack techniques and defence.

References:
1. Stalling, W., “Cryptography and Network Security”, 7th Ed., Prentice Hall, 2011
2. Andrew S. Tanenbaum, Computer Network 5th Ed., Prentice Hall, 2011
3. James F. Kurose and Keith W. Ross, Computer Networking: A Top-Down Approach 6th Ed., Addison-Wesley, 2013

MII 6452 Computational Intelligence and Machine Learning (3 credits)
Prerequisites: Artificial Intelligence

Course Outcomes:
CO-1: Students be able to explain the difference between soft-computing and artificial intelligence characteristics as well as soft-computing methods and its applications.
CO-2: Students be able to explain the basic concepts of evolutionary computation and utilize it to develop an efficient solution for real problems.
CO-3: Students be able to explain the definition of swarm intelligence algorithm and its applications as well as to apply one of the swarm intelligence algorithm (PSO) for solving the real problem.
CO-4: Students be able to explain the support vector machine, kernel, and its applications.
CO-5: Students be able to explain basic concepts of hybrid systems.

Syllabus: Softcomputing and AI Characteristic, Softcomputing methods and applications, Genetic Algorithm, Genetic Programming, Swarm Intelligence Definitions and Algorithms, PSO, SVM, Hybrid System and Its Applications.

References:
1. Russell Elberhart, Computational Intelligence: Concept to Implementation, Morgan Kaufmann, 2007.
2. Rutkowski Leszek, Computational Intelligence Methods and Techniques, Springer- Verlag, 2010.
3. Marsland, S., Machine Learning: An Algorithmic Perspective. CRC Press. 2009.
4. A.E. Elben and J.E. Smith, Introduction to Evolutionary Computing, Springer, 2010.
5. D.E. Goldberg, Genetic Algorithm in search, optimization, and machine learning, Addison-Wiley, 1989.
6. Simon O. Haykin, Neural Networks and Machine Learning, 3rd Edition, Prentice Hall, 2008.

MII 6818 Parallel Computation (3 credits)
Prerequisites: Analysis of Algorithm

Course Outcomes:
CO-1: Students be able to calculate the parallel algorithm travel time.
CO-2: Students be able to formulate the complexity of parallel algorithm with asymptotic notation.
CO-3: Students be able to apply abstract data type in a parallel computing environment.
CO-4: Students be able to explain the theory and apply processing architecture for parallel computing (shared memory, distributed memory, GPGPU).
CO-5: Students be able to explain the theory and apply the decomposition of serial problems into parallel (eg SPL, graph, series).
CO-6: Students be able to use a parallel algorithm for optimization method.
CO-7: Students be capable of programming and installing parallel systems with multiprocessor and GPGPU.

Syllabus: Systems of linear equations, graphs, and series; Decomposition of serial problems into parallel; Abstract data types in parallel computing environments (structs, vectors, etc.); Analysis of time and space complexity of parallel algorithm; Parallel computing process architecture (shared memory, distributed memory, GPGPU); Parallel algorithm for optimization method; Programming and installation of parallel systems with multiprocessor and GPGPU.

References:
1. Sanders J., Kandrot E., CUDA by Example: An Introduction to General-Purpose GPU Programming, NVIDIA, 2011.
2. J. Quinn, M., Parallel Programming in C with MPI and OpenMP, McGraw-Hill Education,2008.

MII 6816 Cryptology (3 credits)
Prerequisites: -

Course Outcomes:
CO-1: Students be able to explain the general theory related to number theory, algebra field, arithmetic logarithm.
CO-2: Students be able to calculate the complexity of encryption and decryption operations.
CO-3: Students be able to apply the symmetry cryptosystem algorithm (eg classical cryptography, Feistel structure, DES, AES).
CO-4: Students be able to apply the asymmetry cryptosystem algorithm (eg: Diffie-Helman, RSA, Elliptic curve, El Gamal).
CO-5: Students be able to analyze cryptosystem using statistical probability, and brute force.

Syllabus: Theory of Numbers, Algebra field, Logarithm of modular arithmetic; The complexity of encryption and decryption operations; Classical symmetry cryptography, Feistel structure, Data Encryption Standards, Advanced Encryption Standards; Cryptosystem asymmetry Diffie- Helman, RSA, Elliptic curve, El Gamal; Cryptosystem analysis (statistical probability, and brute force).

References:
1. Menezes, A, P. Van Ooschot, dan S. Vanstase, Handbook of Applied Cryptography, CRC Press, 1996.
2. Brown, L, Cryptography and Computer Security, Australian Defense National Academy, 2001.
3. Stalling, W., Cryptography and Network Security: Principles and Practice (7th Edition), Pearson, 2016.
4. Patterson, W., Mathematical Cryptology for Computer Scientist and Mathematicians, Rowman and Littlefield Publishers, 1987.
5. Bose, R., Information Theory, Coding, and Cryptography, McGraw Hill, 2003. Stinson, D.R., Cryptography: Theory and Practice, CRC Press, London, 1995.

MII 6454 Fuzzy Logic (3 credits)
Prerequisites: Artificial Intelligence

Course Outcomes:
CO-1: Students be able to explain the basic concepts of fuzzy number, crisp and fuzzy set, and their basic operations.
CO-2: Students be able to explain and use fuzzy set operator in general.
CO-3: Students be able to explain the type of fuzzy relations.
CO-4: Students be able to explain the basic concept of linguistic variables and fuzzy logic and their applications.
CO-5: Students be able to formulate fuzzy rules from knowledge or information to solve the real problem
CO-6: Students be able to explain fuzzy inference machine and use it to solve the real problem.
CO-7: Students be able to explain the component of fuzzy system and to design the fuzzy system.

Syllabus: Classical Logic, Fuzzy Logic, Definition of Fuzzy Numbers and Fuzzy Set, Types of fuzzy relations, Definitions and components of linguistic variables, Fuzzy Rules and Components; Fuzzy inference methods, Fuzzy System Definitions; fuzzy system components; fuzzification; defuzzification.

References:
1. Wang, L., A Course in Fuzzy Systems and Control, Prentice-Hall International, Inc., New Jersey, 1997.
2. Zimmerman, H.J., Fuzzy Set Theory and Its Applications, Kluwer Publishing Co, Amsterdam, 1991.
3. Kaufmann, A. and M.M. Gupta, Introduction to Fuzzy Arithmetic Theory and Applications, Van Nostrand Reinhold, 1991.
4. Klir, G.J. and T.A. Folger, Fuzzy Sets Theory: Foundation and Application, Prentice Hall, 1997
5. Roos, T.J., Fuzzy Logic with Engineering Application, John Wiley & Sons, Ltd., 2010.

MII 6634 Mathematics for Data Science (3 credits)
Prerequisites: -

Course Outcomes:
CO-1: Students be able to explain basic probability theory, multivariate distributions, laws of large numbers, and central limit theorem.
CO-2: Students be able to explain maximum posterior estimation, maximum likelihood estimation, minimum mean-squared error, and confidence intervals.
CO-3: Students be able to understand the basic concepts of linear algebra: vector spaces, linear transformations, singular value decomposition, eigen decomposition, and principal component analysis
CO-4: Students be able to explain matrix calculus, gradient descent, coordinate descent, and introduction to convex optimization.

Syllabus: Probability and statistics inference (multivariate distributions, laws of large numbers, central limit theorem, confidence intervals), estimation and error (maximum posteriori estimation, maximum likelihood estimation, minimum mean-squared error), vector spaces, linear transformations, singular value decomposition, eigen decomposition, principal component analysis), matrix calculus (gradient descent, coordinate descent, introduction to convex optimization).

References:
1. Hill, T., Lewicki, P., & Lewicki, P., Statistics: methods and applications: a comprehensive reference for science, industry, and data mining. StatSoft, Inc., 2006.
2. Kreyszig, E., Advanced engineering mathematics. John Wiley & Sons, 2010.
3. Hastie, T., Tibshirani, R., & Friedman, J., The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Biometrics, 2002.
4. Golub, G. H., & Van Loan, C. F., Matrix computations (Vol. 3). JHU Press, 2012.

MIK 6212 Automata, Language dan Application (3 credits)
Prerequisites: Familiar with Set Theory

Course Outcomes:
CO-1: Students be able to explain the set theory and its relations.
CO-2: Students be able to explain Grammar theory, regular expression, and CFG.
CO-3: Students be able to explain the finite automata theory, DFA, NDFA, PDA, and Turing Machine.
CO-4: Students be able to apply syntax checking modelling.

Syllabus: 1. The general concept of relations and its applications. 2. Regular languages and expressions, grammar, and Free Grammar Context. 3. Limited Automata, Detitefinite Finite Automata, Automata Limited Without Deterministic, Push Down Automata, Turing Machine. 4. Application in Model checking, syntax checking.

References:
1. Martin, J.C., Introduction to Languages and the Theory of Computation, Mc Graw Hill, New York, 1997.
2. Lewis, H.R and C.H. Papadimitrion, Elements of the Theory of Computation, Prentice Hall, Englewood, 1981.
3. Rich, E., Automata, Computation, and Complexity: Theory and Applications, Paerson Prentice Hall, 2009.
4. Linz, P, An Introduction to Formal languages and Automata, Fifth Edition, John and Bartlett Learning, 2012

MII 6896 Digital Signal Processing (3 credits)
Prerequisites: Electronics and Instrumentations

Course Outcomes:
CO-1: Students be able to explain the basic concept of DSP;
CO-2: Students be able to explain the concept of sampling in the sphere of time;
CO-3: Students be able to explain concept and apply signal representation transformation from time domain to frequency domain;
CO-4: Students be able to apply the Z transform to solve different equations;
CO-5: Students be capable of designing and applying digital filters;

Syllabus: This Digital Signal Processing Course provides a basic overview of the concepts of signal processing in the digital realm that contain the basic concept, the concept of signal sampling in the realm of time, the concept and application of signaltransformation from the time domain to the frequency domain using Fourier transform, concepts, design and application of FIR filter (Finite Impulse Response) and type IIR (Infinite Impulse Response) digital.

References:
1. Isen, F.W., DSP for MATLAB and LABVIEW Vol. I: Fundamentals of Discrete Signal Processing, Morgan & Claypool Publisher;
2. Isen, F.W., DSP for MATLAB and LABVIEW Vol. II: Discrete Frequency Transforms, Morgan & Claypool Publisher;
3. Isen, F.W., DSP for MATLAB and LABVIEW Vol. III: Digital Filter Design, Morgan & Claypool Publisher;

MII 6856 Expert Systems (3 credits)
Prerequisites: Artificial Intelligence

Course Outcomes:
CO-1: Students be able to explain the expert system architecture and expert system components and able to design expert systems for real settlement.
CO-2: Students be able to represent and manipulate knowledge in the form of production rules, frames and semantic networks
CO-3: Students be able to explain the methods of inference and reasoning process in the expert system
CO-4: Students be able to identify uncertain factors and apply methods of handling uncertainty.
CO-5: Students be able to apply the stages in developing expert system
CO-6: Students be able to conduct research for expert system development as a real problem solving and explain the result of the research

Syllabus: Components of expert systems, Representation of knowledge Rules, Frame, Semantic Networks, Inference Methods, Causes Factors and methods of handling uncertainty, Stages of expert system development, expert system applications.

References:
1. Joseph C. Giarratano and Gary Riley, Expert Systems: Principles and Programming, Brooks Cole, 4th Edition, Thomson Course Technology, 2005
2. Efraim Turban, Decision Support and Expert Systems: Management Support System, Prentice-Hall, 4th Edition, 1995
3. Ivan Bratko, Prolog-Programming for Artificial Intelligence, Addision Wiley, 3rd Edition, 2001.

MII 6858 Decision Support Systems (3 credits)
Prerequisites: Database System

Course Outcomes:
CO-1: Students be able to explain the types of decisions and decision-making models, and to apply decision-making mechanisms
CO-2: Students be able to explain the definition of business intelligence, knowledge-based for business intelligence and business intelligence components
CO-3: Students be able to explain the components of the decision support system and its functions, and design the decision support system for real problems
CO-4: Students be able to explain the definition of Group-DSS, Group-DSS components and group decision-making methods
CO-5: Students be able to explain the definition of Clinical-DSS, Clinical DSS components, and decision-making methods in CDSS
CO-6: Students be able to explain and apply decision support system development mechanism

Syllabus: Decision-making mechanisms; types of decisions; decision-making, knowledge-based models for business intelligence; business intelligence components, decision support system components, business intelligence definitions, GDSS definitions; GDSS components; group decision-making methods, CDSS definitions; CDSS components; decision-making methods in the CDSS, Mechanisms for the development of decision support systems.

References:
1. Turban, E., Decision Support and Intelligent Systems, Prentice Hall, 2010.
2. Gray, P., Decision Support and Executive Information Systems, Prentice Hall, 1994.
3. Thomas L. Saaty, Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process, RWS Publication, 2000.
4. Eta S. Berner, Clinical Decision Support Systems: Theory and Practice, 2nd Edition, Springer, 2007.

MII 6876 Embedded System (3 CREDITS)
Prerequisites: Operating System and Computer Organization

Course Outcomes:
CO-1: Students be capable of modeling embedded systems.
CO-2: Students be capable of designing and implementing embedded systems.
CO-3: Students be able to analyze embedded system.

Syllabus: This embedded system lecture material uses a saiber-physical system approach consisting of three parts: Modeling, Designing and Analysis. The modeling section discusses continuous dynamics, discrete dynamics, hybrid systems, state machine compositions and concurrent computing models. The design section discusses sensors and actuators, embedded processors, memory architectures, input and output, multitasking and scheduling. The analysis discusses the invariant and temporal logic, equivalents and refinements, achievement analysis and model testing, quantitative analysis as well as security and privacy.

References:
1. Lee, E.A., and Seshia, S.A., Introduction to Embedded Systems: A Cyber-Physical Systems Approach 2nd Edition, MIT Press, 2017.

MII 6453 Computer Reasoning (3 credits)
Prerequisites: Artificial Intelligence

Course Outcomes:
CO-1: Students be able to explain basic concepts of computer reasoning, its differences with rule-based, simulation, visualization and animation as well as differences in computer reasoning and human reasoning
CO-2: Students be able to apply case retrieval techniques and case-counting calculation methods.
CO-3: Students be able to explain the mechanism of adaptation of the solution to the case
CO-4: Students be able to explain concepts and probabilistic paradigms, Bayesian and Bayesian Networking theorems.
CO-5: Students be able to master and apply the basic concept of evidence and methods of pooling in a hierarchy

Syllabus: Definition of Computer Reasoning, Computer System Reasoning Methods, Case Adaptation Mechanisms, Bayes Theorem, Bayesian Network, Basic Concepts of Evidence, Hierarchical Pooling Methods.

References:
1. Hullermer, Eyke, Case-based Approximate Reasoning, Springler, 2007.
2. Neapolition, Ricard E, Probabilistic Reasoning in Expert System: Theory and Algorithm, 2012.
3. Pearl, J., Probabilistic Reasoning in Intelligence System: Network of Plausible.

MII 6835 Semantic Search and Information Retrieval (3 credits)
Prerequisites: Artificial Intelligence *

Course Outcomes:
CO-1: Students be able to explain the architecture of Search Engine and its components.
CO-2: Students be able to explain the stage of making an index and able to do text acquisition, text transformation, and index creation.
CO-3: Students be able to explain various types of queries and be able to rank query results 4.
CO-4: Students be able to evaluate Search Engine
CO-5: Students be able to explain concepts related to web semantics and ontology and able to apply the concept for ontology manufacture
CO-6: Students be able to explain the components of semantic search engines.

Syllabus: The main topic discussed in this course are: The basic IR model, the text equality metric; TF-IDF; pre-text processing, experimental evaluation of IR; Query and language operations; Text Representations such as in the form of word stats, Zipf Law, Web Search Engine, metacrawlers, and examples; Text Classification and models of question and answer system; Language-based Retrieval model, Recommended System; Collaborative filtering, as well as Information Extraction and Integration, the concept of Web Semantics and Ontology, ontology development, F-logic, Logic descriptions, extraction of symbolic knowledge; analysis of social networks

References:
1. Chowdhury, G. G., Introduction to modern information retrieval. Facet publishing, 2010.
2. Hang Li, and Jun Xu, Semantic Matching in Search, 2014.
3. Federico Alberto Pozzi, Messina & Liu, Sentiment Analysis in Social Networks, 1st Edition, Morgan Kaufmann, 2016.
4. Bast, H., Buchhold, B., & Haussmann, E., Semantic search on text and knowledge bases, 2016.

MII 6455 Patterns Recognition (3 credits)
Prerequisites: -

Course Outcomes:
CO-1: Students be able to explain the types of data in a pattern recognition
CO-2: Students be able to explain feature definitions and feature extraction methods and perform feature extraction.
CO-3: Students be able to explain the basic concepts of classification and methods of classification, and to do the classification in pattern recognition.
CO-4: Students be able to explain differences in classification and clustering, types of clustering, as well as clustering data in pattern recognition.
CO-5: Students be able to explain the development of research on pattern recognition.

Syllabus: Types of text, image, audio and video data, Classification and methods, Clustering and methods, Mechanisms and methods of identification and verification, The development of research on pattern recognition.

References:
1. Theodoridis, S. and Koutroumbas, K., Pattern Recognition. 4th Edition, Academic
Press, 2008.
2. Richard O. Duda, Peter E. Hart, and David G. Stork., Pattern Classification, John Wiley & Sons, 2000.
3. Bishop, C. M. Neural Networks for Pattern Recognition. Oxford University Press. 1995.
4. Bishop, C. M. Pattern Recognition and Machine Learning. Springer. 2007.

MII 6895 Digital Image Processing (3 credits)
Prerequisites: Pattern Recognition*

Course Outcomes:
CO-1: Students be able to explain the basic concept of DIP
CO-2: Students be able to translate DIP problems into design
CO-3: Students be able to use mathematical model and physics and related software for DIP
CO-4: Students be able to implement DIP design in the form of program
CO-5: Students be able to learn more from the latest development of DIP

Syllabus: Image representation, sampling, quantization, pixel-depth, RGB, HSV, CYMK, YcbCr, algorithms (time domain, frequency domain), image object separation (threshold, area, movement, edge), feature representation, feature selection, interpretation.

References
1. Gonzalez, R.C., Wintz, P., Digital Image Processing, Pearson Higher Ed. USA, 4th ed, 2017
2. Pratt, W.K., Introduction to Digital Image Processing, CRC Press, 2014

MII 6213 Operation Research (3 credits)
Prerequisites: Linear Algebra

Course Outcomes:
CO-1: Students be able to formulate and solve real-world problems into Linear Programming and Non-Linear Programming.
CO-2: Students be able to apply the simplex method to find a solution of a linear program.
CO-3: Students be able to apply the gradient descent method to search for nonlinear program solution.
CO-4: Students be able to solve transportation problems, assignment, transhipment.
CO-5: Students be able to apply PERT-CPM computing to the scheduling process.
CO-6: Students be able to solve integer programming problems.

Syllabus: Problem-solving with linear programming and nonlinear programming; Linear Programming: understanding, graphics solution, work-scheduling problem,capital-budgeting problem; Simplex Method: Simplex Algorithm and its use in optimization problem, 2 phase Simplex Method; Gradient Descent Method; Transportation, assignment, transshipment; Scheduling issues: PERT-CPM; Integral programming: introduction, problem formulation, Branch and Bound Method, Cutting Plane Algorithm.

References:
1. Winston, W.L., Operations Research: Applications and Algorithms (with CD-ROM and InfoTrac) 4th Edition, Duxbury Press, 2003.
2. Taylor, B.W., Introduction to Management Science 11th edition, Pearson, 2013.

MII 6893 Robotics (3 credits)
Prerequisites: Familiar with robotics system

Course Outcomes:
CO-1: Students be able to explain basic concepts of Robotics
CO-2: Students be able to analyze Robotics system
CO-3: Students be able to design a robotics system
CO-4: Students be able to perform the component selection, and improve the ability of simple robots
CO-5: Students be able to improve performance or robot properties

Syllabus: in out, open-ended system, closed-loop, response output, mathematical model, ADC, DAC, control components, controls, sensors, actuators, tracking, motion planning, motion control, mechanical-reflex, parallel thinking, serial thought, dynamics, AI reasoning, the ability of the human brain, instinct, the use of artificial intelligent, swam, unmanned system.

References:
1. Jazar, R. N., Theory of Applied Robotics Kinematics, Dynamics, and Control, 2nd ed, Elsevier, 2010

MII 6815 Computational Science (3 credits)
Prerequisites: Analysis of Algorithm

Course Outcomes:
CO-1: Students be able to explain the theory and apply polynomial algebra.
CO-2: Students be able to calculate the computing time of operations on the polynomial.
CO-3: Students be able to calculate complexity of SPL and SPNL completion algorithm.
CO-4: Students be able to apply optimization methods of a SPNL without and with restrictions.
CO-5: Students be able to apply a method to find polynomial roots, optimum point on one or more dimensional nonlinear functions, and smoothing function.
CO-6: Students be able to analyze the error of polynomial root search method, optimum point on one or more dimensional nonlinear function, and smoothing function.

Syllabus: Polynomial Algebra and its applications; Computational time of operations on polynomials; Complexity of linear, nonlinear solution solving algorithms; Search solution with interior point method; Optimization of SPNL problems with and without restrictions; Search for polynomial roots, optimum point on nonlinear functions of one or more dimensions, smoothing function; Theory of error / error analysis on polynomial root search, optimum point on nonlinear function of one or more dimensions, smoothing function.

References:
1. Winkler, F., Polynomial Algorithms in Computer Algebra, Springer-Verlag, 1996.
2. P. Bertsekas, D., Nonlinear Programming 2nd Edition, Athena Scientific, 1999.

MII 6291 Control Systems (3 credits)
Prerequisites: Instrumentation

Course Outcomes:
CO-1: Students be able to explain the concept of digital control
CO-2: Students be able to apply Z transform to analyze mechanical, electrical, electronic, fluid and thermal system
CO-3: Students be able to explain the principle of error analysis and control system response settings
CO-4: Students be able to apply stability analysis method to improve system performance
CO-5: Students be capable of using SciLab / Matlab for digital control simulation
CO-6: Students be able to model system with state-space representation.

Syllabus: block diagram, open loop, closed loop, stability analysis (root position in Z domain (pole, zero), Jury test method, Nyquist, asymptotic, bibo, internal, input, output), adaptive control method, optimal control method.

References:
1. Fadali, M.S., Visioli, A., Digital Control Engineering Analysis and Design, Elsevier, 2013
2. Ogata, K., Modern Control Engineering, 5th ed, Prentice Hall, 2010.

MII 6875 Mobile and Wireless Communication Systems (3 credits)
Prerequisites: Computer Networks

Course Outcomes:
CO-1: Students be able to explain the concept and standard of a wireless network, wireless LAN / MAN, and mobile network (cellular).
CO-2: Students be able to explain teletraphic techniques and analytic models on mobile networks.
CO-3: Students be able to explain the concept and design the sensor network architecture.
CO-4: Students be able to explain the quality of service (QoS) in wireless and mobile networks.

Syllabus: The course of a wireless and mobile communication system is about the concept and standard of a wireless and mobile network, antenna function and forward error correction, wireless network application on the sensor network. Teletraphic theory and analytic model on mobile networks. The concept and architecture of sensor networks, topologies, and sensor network applications. Naming, addressing, and routing on the sensor network. Quality and service quality parameters (QoS) and QoS in wireless and mobile networks.

References:
1. Agrawal, D.P., Zeng, Q., Introduction to Wireless and Mobile Systems, Thomson, 2003
2. Stasiak, M., Glabowski, M., Wisniewski, A., Zwierzykowski, P., Modeling and Dimensioning of Mobile Networks: from GSM to LTE, John Wiley and Sons, 2011.
3. Holger Karl, PROTOCOLS AND ARCHITECTURES FOR WIRELESS SENSOR NETWORKS, John Wiley & Son Ltd., 2005
4. Liljana Gavrilovska, Application and Multidisciplinary Aspects of Wireless Sensor Networks, Springer-Verlag, London, 2011.

MII 6671 Cloud and Distributed Systems (3 credits)
Prerequisites: Computer Network and Operating System

Course Outcomes:
CO-1: Students be able to explain distributed systems, system models, cluster computers, and virtual machines
CO-2: Students be able to apply Cloud system concept and architecture, web service and SOA, programming on Cloud system
CO-3: Students be able to explain the concept of Grid, P2P, and Internet of Things (IoT)
CO-4: Students be able to apply HPC concept, MPI programming model and Hadoop Mapreduce
CO-5: Students be able to apply the concept of 3-tier, multitier, and distributed and Cloud system applications

Syllabus: Distributed and Cloud system courses provide conceptual understanding and development of research on distributed systems, system models, clusters and virtual machines (including virtualization concepts). Cloud Architecture with Web Service / Service Oriented Architecture technology and programming on Cloud system. This course also explains the concept of Grid, P2P and IoT as implementations and applications of distributed systems and the cloud. Programming on HPC models in Hadoop and MapReduce environments as well as big data ecosystems to run distributed and cloud-based data-processing applications.

References:
1. Lai Hwang, Geogffrey C Fox, Jack J Dongara, Distributed and Cloud Computing, From Parallel Processing to the Internet of Things, 2011
2. Kerry Koitzsch, Pro Hadoop Data Analytics: Designing and Building Big Data Systems using the Hadoop Ecosystem 1st Edition, 2016.

MII 6857 Bio-inspired system (3 credits)
Prerequisites: Artificial Intelligence

Course Outcomes:
CO-1: Students be able to explain the characteristics of a cellular system
CO-2: Students be able to explain the definition, workings and computation of DNA
CO-3: Students be able to explain the rewriting system; immune system biologically; artificial immune system
CO-4: Students be able to explain the artificial nervous system and apply it to a real solution.
CO-5: Students be able to explain various algorithms based on collective systems and apply for real problems.

Syllabus: Cellular system, DNA, DNA computation, rewriting system, Biological immune system; Artificial immune system, Artificial neural system and its application, Collaborative system-based algorithm and its application.

References:
1. D. Floreano and C. Matiussi, Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies, MIT-Press, 2008.
2. F. Neumann and C. Witt., Bioinspired Computation in Combinatorial Optimization: Algorithms and their computational complexity, Springer, 2010.
3. Bishop, C. M. Neural Networks for Pattern Recognition. Oxford University Press. 1995.
4. M. Dorigo and T. Stutzle, Ant Colony Optimization, A Bradford Book, 2004.
5. R. C. Elberhart et al., Swarm Intelligence, Morgan Kaufmann, 2001.

MII 6837 Social Informatics (3 credits)
Prerequisites: -

Course Outcomes:
CO-1: Students be able to explain the philosophy of technology and culture, researches and methods used
CO-2: Students be able to explain the information society/network/ digital and digital lifestyle
CO-3: Students be able to explain the application of UU ITE
CO-4: Students be able to identify new media development along with its ethics and anticipation
CO-5: Students be able to formulate new ideas about the researches and methods used

Syllabus: This course provides knowledge of technological and cultural philosophy, research and methods on social informatics. This course also discusses the development of information society and digital lifestyle. Implementation and implications of the ITE Law along with the development of new media with ethics discussed to explore the potential of social informatics. New ideas that are destructive to the pattern that has been running into studies to explore new ideas in research in the field of social informatics.

References:
1. Pnina Fichman, Howard Rosenbaum, Social Informatics: Past, Present and Future, 2016.
2. Rob Kling & Howard Rosenbaum, Understanding and Communicating Social Informatics: A Framework for Studying and Teaching the Human Contexts Of Information And Communication Technologies, 2014.

MII 6817 Verivication and Validation (3 credits)
Prerequisites: Logic and Automata

Course Outcomes:
CO-1: Students be able to explain graph theory.
CO-2: Students be able to calculate the complexity of graph search process.
CO-3: Students be able to apply formal modeling theory to hardware and software, parallelism, and communication.
CO-4: Students be able to apply model checking theory on reactive system.

Syllabus: Graph theory and the complexity of the graph search process; Formal modelling theory for hardware, software, parallelism, and communication and its application; Reactive systems and modelling; Theory of checking models and their applications, especially on reactive systems.

References:
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: Modelling, Specification and Verification, Cambridge University Press, 2007.
4. Hyde, D.C., Introduction to the Principles of Parallel Computation, Department of Computer Science, Bucknell University, Lewisburg PA 17837, 1998.