Techniques of Artificial Intelligence

Professor Ann Nowe and Professor Hugues Bersini

Teaching assistants: Bart Jansen and Peter Vrancx

The exam took place on Thursday, January 19, 2012 from 14h to 17h in room D005.

When : From October 3rd, 2011. Mondays - 1pm till 3pm.

The courses will take place at the VUB campus, Building G, Room G1.023

Friday, December 16, 2011: exercise session using WEKA in the computer room K3.110 which is PC room 3.

Note: The exercises on Friday, November 4, 2011 were cancelled.

When: From October 14th, 2011. Fridays - 2pm to 5pm - VUB campus, Building K, Room K7. The location of the exercises has been moved to Schoofslaan 8, lokaal 51/53 (across Triomflaan, close to Colruyt).

Learn to apply AI-techniques on real life engineering case studies.

Basics of statistics and programming.

• Introduction to AI + state representation and search
• Learning of concepts (version spaces and decision trees)
• Datamining
• Reinforcement Learning
• Bayesian Learning
• Neural Networks
• Evaluation of hypotheses
• Computational learning theory
• Optimisation
• Support vector machines
• Ant-algorithms
• Genetic algorithms

1. 3/10 Introduction to AI
2. 10/10 State space search
3. 17/10 Concept learning and decision trees
4. 24/10 Neural networks 1
5. 31/10 Neural networks 2 - This lecture will take place!
6. 7/11 Evaluation of hypothesis and computational learning theory
7. 28/11 Clustering
8. 14/11 Data, Text and Graph mining 1
9. 21/11 Data, Text and Graph mining 2
10. 5/12 Reinforcement Learning
11. 12/12 Metaheuristics: Genetic Algorithms
12. session on the project will be organized end of January/beginning of february, exact date will be announced later.

Handbook Tom Mitchell ( slides)

Handbook Artificial Intelligence: A Modern Approach, by Russel and Norvig (http://aima.cs.berkeley.edu/)

Handbook The essence of Artificial Intelligence, by Alison Cawsey (http://www.macs.hw.ac.uk/~alison/essence.html) (Chapters 1-4)

1. Introduction | pptx
2. State Space Search | ppt
3. Concept Learning
4. Decision Trees
5. Evaluation of Hypothesis (only slides 74-89)
6. Computational Learning Theory (only slides 175-194)
7. Cluster analysis (Presentation)|Data clustering: a review (a useful tutorial)
8. Data Mining
9. Text Mining
10. Reinforcement learning
11. Genetic algorithms

• Lesson 1: slides
• Lesson 2: slides
• Lesson 3&4: slides
• Lesson 5: slides
• Lesson 6: part 1 and part 2 and Pagerank
• Lesson 7&8: Neural Networks
• Lesson 9: Introduction to SVM
• Lesson 10: slides part 1 (only slides 74-89) and part 2 (only slides 175-194)

• Lesson 11: Optimization Metaheuristics
• Lesson 12: Bioinspired: part 1 and part 2
• Lesson 13 Dec: Cluster analysis and Data clustering: a review

VUB/ULB students: Exam+Project.

VUB students (optional for ULB students): Additional assignment related to the exercises sessions, as a preparation for the project.

Project ULB Students

Project VUB Students