Undergraduate Handbook - School of Computing and Informatics ...

engineering approach, analysis design and construction of reusable components, classification and retrieval
of components, economic impact of reuse and reuse metrics.
• Computer aided software engineering: CASE tools and application.
Reference Books:
i. J. F. Peters, W. Pedrycz, Software Engineering: An Engineering approach, John Wiley, 2000.
ii. R. S. Pressman, Software Engineering: A Practitioners Approach, 5th edition, McGraw Hill, 2005.
iii. I. Sommerville, Software engineering, 8th edition, Addison Wesley, 2008.
iv. D. Ghezzi, M. Jazayeri, D. Mandrioli, Fundamentals of Software Engineering, Prentice Hall of India, 2004.
CSC 2114 Artificial Intelligence (3 CU)
Course Description: This course examines the concepts, techniques, applications, and theories of Artificial
Intelligence. The focus of the course is on the theory and application of artificial intelligence. Topics include logic,
search, and reasoning with an emphasis on fundamentals and recent advances in AI. Given the broad range of topics
addressed by the AI field, topics for discussion must, necessarily, be limited. Therefore, this course will focus on
issues of search, knowledge representation, and reasoning, decision making and learning from the perspective of an
intelligent agent.
Indicative Content:
• Introduction to Artificial Intelligence: Simulation of Intelligence behavior, in different areas;
• Problem solving: games, natural language question answering, visual perception, learning; Aim-oriented
(heuristic) algorithms versus solution-guaranteed algorithms.
• Understanding Natural Languages: Parsing techniques, context free and transformational grammars,
transition nets, augmented transition nets, grammar-free analyzers, sentence generation.
• Knowledge Representation: First-order predicate calculus; PROLOG and LISP languages; Semantic nets;
partitioned nets; Production rules; knowledge base, the inference system, forward and backward deduction.
• Expert System: Existing systems (DENDRAL, MYCIN), Domain exploration; Meta-knowledge, expertise
transfer, self explaining systems.
• Pattern Recognition Structured Descriptions: Symbolic description, m
• Machine perception, line finding, interpretation, semantics and models, object identification and speech
recognition.
Reference Books:
i. R. Duda, P. Hart, Pattern Classification and Scene Analysis, Wiley, 1973.
ii. E. A. Feigenbaum, J. Feldman; Computers and Thoughts, AAAI Press, 1995.
iii. N. J. Jilsson, Problem Solving Methods in Artificial Intelligence, McGraw-Hill, 1971.
iv. J. Lloyd, Foundation of Logic Programming, Springer-Verlag, 1993
CSC 2200: Operating Systems (4 CU)
Course Description: Operating Systems course introduces students to software that controls hardware and makes
the hardware usable. Its interaction with other computer devises and how it controls other computer processes is
explored. The Course aims at providing students with a detailed understanding of how operating systems work;
skills to write basic Programmes to utilize underlying operating system infrastructures. At the end of the course,
students are able to know the dominant categories of operating systems are Windows and UNIX (Linux, Mac OS,
Solaris, etc). Students of operating systems are expected to have a proper understanding of the differences between
these two. Students should be able to understand different design principles for operating systems and various
software tools that make operating systems usable.
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