Principles of Artificial Intelligence (COE206)
Spring 2019-2020 (14 weeks, 56 hours)
Wednesday @ 08:30-10:10 (1 ↣ 14 weeks: T-125), Thursday @ 08:30-10:10 (1 ↣ 14 weeks: UNIX Lab)
February 19 - May 28, 2020
Instructor: Mustafa MISIR (Office: 203/G @ ISU Topkapi Campus), mustafa.misir [at] istinye.edu.tr
Artificial Intelligence (AI) refers to the systems act or think like human / act or think rationally. AI is a broad field including various topics such as problem solving, logic, perception, reasoning and learning. This course will introduce a subset of those topics, supported by use-cases and application domains. The given theoretical aspects will be clarified and practiced by the lab sessions.
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Course Book
Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig (3rd Edition), 2010, Prentice Hall
Supplementary reference(s):
- Artificial Intelligence - With an Introduction to Machine Learning by Richard E. Neapolitan, Xia Jiang (2nd Edition), 2018, CRC Press
- The Quest of Artificial: a History of Ideas and Achievements by Nils J. Nilsson (1st Edition), 2018, Cambridge University Press (Free Book)
- Artificial Intelligence: Foundations of Computational Agents by David L. Poole, Alan K Mackworth (2nd Edition), 2017, Cambridge University Press (Free Book)
- Artificial Intelligence Basics: A Non-Technical Introduction by Tom Taulli (1st Edition), 2019, Apress
- Artificial Intelligence: A Guide to Intelligent Systems by Michael Negnevitsky (2nd Edition), 2004, Addison-Wesley
- Artificial Intelligence Safety and Security by Roman V. Yampolskiy (Ed. 1st Edition), 2019, CRC Press
- Artificial Intelligence: Structures and Strategies for Complex Problem Solving by George F. Luger (6th Edition), 2009, Addison-Wesley
- Intelligent Systems: A Modern Approach by Crina Grosan, Ajith Abraham (1st Edition), 2011, Springer
- Artificial Intelligence: A New Synthesis by Nils J. Nilsson (1st Edition), 1998 Morgan Kaufmann
- Artificial Intelligence Illuminated by Ben Coppin (1st Edition), 2004, Jones and Bartlett
- Introduction to Algorithms by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein (3rd Edition), 2009, MIT Press
- Algorithms by Robert Sedgewick, Kevin Wayne (4th Edition), 2011, Addison-Wesley
Lecture Notes / Slides
- L0 + L1: Introduction to Artificial Intelligence: a general discussion on the course together with an introduction to AI with real-world applications from a historical perspective
- L2: Intelligent Agents: describing intelligent agents with related concepts and types
- L3: Problem Solving (++ Graphs): providing a formal definition of problem solving through search with example scenarios
- L3-1: Uninformed Search: presenting the basic blind search approaches
- L3-2: Informed Search: offering alternative (heuristic) search methods operating through evaluation functions, to blind search
- L4: Local Search: as a third search option, illustrating principal local search techniques
- L5: Adversarial Search: exhibiting problem solving strategies for games with opponents
- L6: Constraint Satisfaction Problems: demonstrating a family of problems described in a constrained manner while disclosing the traditional solution procedures
- L7: Logical Agents: introducing logical agents in the form of knowledge-based agents after revealing the underlying concepts on propositional logic
- L8: Learning Problem: discussing the concept of learning and its identification in the field of machine learning
Grading
- Labs / Assignments / Quizzes: 30%
- Midterm Exam (Take-home): 30%
- Final Exam (Take-home): 40%
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