Course Organizer
CAP 5605 Artificial Intelligence
Spring Semester 2010
Extras: Miscellaneous Resources and References Syllabus Course Syllabus. This explains course grading and other administrative topics. TextBook Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6/E, Addision-Wesley, 2009. This is the required course textbook - the primary resource for the course. Programming AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java. Addison-Wesley, 2009. An excellent and recommended AI programming resource. This is a supplement that is highly recommended for those seeking knowledge in AI programming techniques in Prolog and/or Lisp. Ch3 Sol
Ch4 Sol
Ch6 Sol
Midterm
Assignments and Solutions m&m
Minds & Machines paper - roots of expert network technology scfa
Stanford CFA Notes enbp
Hierarchical Architectures for Reasoning - Computational Network Backpropagation Paper xnet_redacted
Recent gray paper, with newest IP redacted xnet
Expert Technologies research site (rescricted access - send email for permission) ga
Genetic Algorithm Notes
Temporal View: Course Calendar Week Dates Coverage [Chapters refer to Textbook] Links Assignment [Numbers refer to text exercises] Due Date 1 1/6 - 1/10 Abbreviated week - no class 2 1/11 - 1/17 Course Syllabus
Chapter 1: Roots & ScopeSyllabus
Ch1.pptCh1 Exercises 3, 4, 5, 8, 12
Post in Forum1/19 3 1/18 - 1/24 Chapter 2: Predicate Calculus Ch2.ppt 4 1/25 - 1/31 Chapter 3: State Space Search 1 Ch3.ppt Ch 3 Exercises 5, 7, 10 2/3 5 2/1 - 2/7 Chapter 4: Heuristic Search Ch4.ppt Ch 4 Exercises 7, 10 2/10 6 2/8 - 2/14 Chapter 6: State Space Search 2 Ch6.ppt Ch 6 Exercises 5, 8, 9 2/17 7 2/15 - 2/21 Chapter 7: Knowledge Representation Ch7.ppt 8 2/22 - 2/28 Chapter 8: Expert Systems Ch8.ppt 9 Schedule Midterm Exam: See Syllabus for details 10 3/8 - 3/14 Spring Break - no classes 11 3/15 - 3/21 Chapter 9: Reasoning under Uncertainty Ch9.ppt 12 3/22 - 3/28 Chapter 10: Machine Learning 1: Symbolic Ch10.ppt 13 3/29 - 4/4 Chapter 11: Machine Learning 2: Connectionist Ch11.ppt 14 4/5 - 4/11 Chapter 12: Machine Learning 3: Genetic & Emergent Ch11.ppt 15 4/12 - 4/18 Chapter 13: Machine Learning 4: Probabilistic Ch13.ppt 16 4/19 - 4/25 Chapter 16: Future of AI Ch16.ppt 17 Schdeule Final Exam: See Syllabus for details