Lecture notes/slides will be uploaded during the course.
Download Textbook lecture notes
VideoLectures Online video on RL
Please download the above textbook slides. My slides are based on theirs with minor modification
Chapter 1. Introduction (ppt) Chapter 2. Supervised Learning (ppt) Chapter 3. Bayesian Decision Theory (ppt) Chapter 4. Parametric Methods (ppt) Chapter 5. Multivariate Methods (ppt) Chapter 6. Dimensionality Reduction (ppt) Chapter 7. Clustering (ppt) Chapter 8. Nonparametric Methods (ppt) Chapter 9. Decision Trees (ppt) Chapter 10. Linear Discrimination (ppt) Chapter 11. Multilayer Perceptrons (ppt) Chapter 12. Local Models (ppt) Chapter 13. Hidden Markov Models (ppt) Chapter 14. Assessing and Comparing Classification Algorithms (ppt) Chapter 15. Combining Multiple Learners (ppt) Chapter 16. Reinforcement Learning (ppt)
Week 1 (Chapter 1)
https://www.cmpe.boun.edu.tr/~ethem/i2ml3e/3e_v1-0/i2ml3e-chap1.pptx
Week 2 (Chapter 2)
Week 3
lec3.ppt Bayesian Decision Theory
Week 4
lec4.ppt Parametric methods, MLE
Week 5
lec5.ppt Multivariate Methods
Week 6
Week7
lec9.ppt Linear discriminant
Week8
lec10.ppt Neural networks
Week9
lec11.ppt GA, GP
Week10
Midterm March 15.
Week11
lec13.ppt SVM
Week12
lec14.ppt evaluation of classifers
Week13
lec15.ppt HMM
Week14
DimRed.ppt dimension reduction
ensemble.ppt Ensemble Learning Algorithms
Week15
lec17.ppt Reinforcement Learning
Week16
lec18.ppt Graphical models