Artificial Intelligence - Introduction

Podstawy sztucznej inteligencji

Partially offered in English as: Artificial Intelligence: Knowledge Representation and Machine Learning Methods

To give an ample introduction to the classic and most important topics in AI. Additional treatment of Machine Learning topics - mostly symbolic/logical models.

Main:

• [FCA] D. Poole, A. Mackworth, Artificial Intelligence: Foundations of Computational Agents, Cambridge University Press, 2010, ONLINE
• [AIM] Stuart Russell and Peter Norvig Artificial Intelligence: A Modern Approach, 3rd Ed., Pearson, 2010.
• [TAC] Hector Levesque Thinking as Computation, MIT Press, 2012.

Additional:

• [KRR] Ronald Brachman Hector Levesque Knowledge Representation and Reasoning, Morgan Kaufman, 2004
• [KRH] F. van Harmelen, V. Lifschitz, B. W. Porter, Handbook of Knowledge Representation, Elsevier Science (2008), ONLINE
• [SLG] Peter Flach, Simply logical, Wiley, 1994, ONLINE
• [LAI] Stuart C. Shapiro, Knowledge Representation and Reasoning: Logics for Artificial Intelligence, (Lecture notes ONLINE)
• [CIL] D. Poole, A. Mackworth and R. Goebel, Computational Intelligence: A Logical Approach, Oxford University Press, 1998.

Prolog online tutorials:

• [LPN] Learn Prolog Now!
• [AIP] Adventure in Prolog
• [ESP] Expert Systems in Prolog

Prolog books:

• [BRT] I. Bratko, Prolog Programming for Artificial Intelligence, 4th ed, Addison-Wesley, 2011.
• [LPP] Ulf Nilsson and Jan Małuszyński, Logic, Programming and Prolog (2ed). ONLINE
• [PPD] Michael Covington et. al., Prolog Programming in Depth. ONLINE

Varia:

• SWI-Prolog environment

• [DMW] Ian Witten, Eibe Frank, Mark Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd edition, Morgan Kaufmann Publishers, 2011
• [FLA] Peter Flach Machine Learning. The Art and Science of Algorithms that Make Sense of Data, Cambridge University Press, 2012.
• [TML] T. Mitchell, Machine Learning, McGraw Hill, 1997.

Additional:

• [CMB] Christopher M. Bishop Pattern Recognition and Machine Learning, Springer, 2007.
• [KDA] Krzysztof Cios et al. Data Mining A Knowledge Discovery Approach, Springer, 2007 slajdy

Online material:

• [ANG] Andrew Ng Coursera: Machine Learning
• [ZMV] Zdravko Markov, CS570 - Topics in AI: Machine Learning, oraz Machine Learning Programs and Laboratory Experiments in Prolog, 2003.
• Ian Witten, Data Mining with Weka: http://wekamooc.blogspot.com/ https://weka.waikato.ac.nz/explorer
• Andrew Ng, Machine Learning: https://www.coursera.org/course/ml

Varia:

• WEKA
• UCI ML repo
• Zdravko Markov and Daniel T. Larose Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage

1. 2014-03-04: Initial meeting, plans
2. 2014-03-11: Intro to AI:
   • Read: FCA:1,2, AIM:1,2
   • Lab: intro to Prolog, see SWI-Prolog environment
3. 2014-03-18: Catching up with Prolog
   • Read: TAC:1,2,3
   • Lab: using Prolog, LPN:1,2, AIP:1,2,3
4. 2014-03-25: Intro to thinking as computation
   • Read: FCA:3, AIM:3,4
   • Lab: Prolog problem solving, LPN:3, AIP:4,5
5. 2014-04-01: Searching for solutions
   • Read: FCA:4, AIM:6, TAC:5
   • Lab: Prolog knowledge bases, AIP:7-10, see: lab_termy
6. 2014-04-08: Constraints
   • Read: FCA:5,13, AIM:7,8,9,12
   • Lab: Searching
7. 2014-04-15: Knowledge representation, expert systems
   • Lab:Prolog and constraints
   • Lab: Prolog assignements for evaulation
8. 2014-04-29: ML
9. 2014-05-06: ML: octave, weka, decision trees
10. 2014-05-13: ML: rules, and association rules
11. 2014-05-20: ML: linear models and linear regression
12. 2014-06-03: ML: clustering
13. 2014-06-10: ML: Bayesian nets
14. 2014-06-17: final test