Artificial Intelligence Course : Online Learning Resources & Teaching Materials

1 . Course Type : Artificial Intelligence : Online learning resources & teaching materials; Full Course Lectures 42 hrs; Slides, notes include text, tables, diagrams, examples, algorithms, pseudo codes and book references, offered free online in 11 PDF files, total 565 Pages. curriculum standard B.Tech, 6th semester,CSE. Students can learn online; Teachers can use/adopt as part of their teaching material. ; 2 . Course Description : Artificial Intelligence : Full Course Lectures 42 hrs; Topics - Introduction to AI, Problem solving, Knowledge representation, Reasoning system, Game playing, Learning system, Expert system, Neural network, Genetic algorithm, Natural language processing, and Common sense. The course materials are offered free online in PDF files. The contents are more traditional and self explanatory for online learning. Students can learn online; Teachers can use/adopt as part of their teaching material. This course is offered at Jaypee University of Engineering & Technology (JUET). URL : http://www.myreaders.info/html/artificial_intelligence.html ; 3 . Course objectives: Conditions of use : Creative Commons Attribution-Noncommercial-No Derivative Works 2.5. [Ref. http://www.oercommons.org/courses/artificial-intelligence ] (i) The material offers online learning resources for students. (ii) A teacher can use/adopt as part of their teaching material. (iii) The lecture slides, notes includes traditional explanatory text, tables, diagrams, examples, algorithms, pseudo codes and book references. (iv) The Learning objectives are (a) Examining concepts, techniques, applications, and theories of artificial intelligence. The focus is on theory and application of AI. (b) The course intends to teach - Introduction to Artificial Intelligence; Problem Solving - search and control strategies; Knowledge representation issues - predicate logic, rule base; Reasoning system - symbolic, statistical; Game playing; Learning systems; Expert systems; Fundamentals of neural networks; Fundamentals of genetic algorithms; Natural language processing; and Common sense. (c) At the end of this course the student will be able to formulate and assess problems in artificial intelligence; assess the strengths and weaknesses of methods for representing knowledge; the strengths and weaknesses of AI algorithms in areas such as heuristic search, game search, logical and statistical inferencing, decision theory, planning, machine learning, and natural language processing; implement software solutions to a wide variety of problems generally considered requiring artificial intelligence. (v) Skills desired - familiar with symbolic logic, basic probability theory, data structure, object oriented programming. ; 4 . Conditions of Use: Creative Commons Attribution-Noncommercial-No Derivative Works 2.5 [Ref. http://www.oercommons.org/courses/artificial-intelligence ]