| Date |
Lecture |
Notes etc |
| Wed Sep 09 |
Lecture 1: Introduction, linear classification, perceptron
|
lecture1_slides.pdf, lecture1_notes.pdf
|
| Mon Sep 14 |
Lecture 2: perceptron analysis, support vector machine
|
lecture2_slides.pdf, lecture2_notes.pdf
|
| Wed Sep 16 |
Lecture 3: non-linear classification, kernels
|
lecture3_slides.pdf, lecture3_notes.pdf
|
| Mon Sep 21 |
Lecture 4: kernels, support vector machines
|
lecture4_slides.pdf, lecture4_notes.pdf, tutorial notes on Lagrange multipliers lagrange.pdf
|
| Wed Sep 23 |
Lecture 5: anomaly detection
|
lecture5_slides.pdf, lecture5_notes.pdf
|
| Mon Sep 28 |
Lecture 6: multi-way classification, rating, ranking
|
lecture6_slides.pdf, lecture6_notes.pdf
|
| Wed Sep 30 |
Lecture 7: ranking (cont'd), regression, collaborative filtering
|
lecture7_slides.pdf, related notes lecture7_notes1.pdf, lecture7_notes2.pdf
|
| Mon Oct 05 |
Lecture 8: collaborative filtering (cont'd), feature selection
|
lecture8_slides.pdf
|
| Wed Oct 07 |
Lecture 9: ensembles, boosting
|
lecture9_slides.pdf, lecture9_notes.pdf
|
| Tue Oct 13 |
Lecture 10: active learning
|
lecture10_slides.pdf, lecture10_notes.pdf
|
| Wed Oct 14 |
MIDTERM EXAM
: open book
|
| Mon Oct 19 |
Lecture 11: model selection, complexity
|
lecture11_slides.pdf, lecture11_notes.pdf
|
| Wed Oct 21 |
Lecture 12: VC-dimension, generalization guarantees
|
lecture12_slides.pdf, lecture12_notes.pdf
|
| Mon Oct 26 |
Lecture 13: mixture models, the EM algorithm
|
lecture13_slides.pdf, lecture13_notes.pdf
|
| Wed Oct 28 |
Lecture 14: mixtures (cont'd), EM algorithm
|
lecture14_slides.pdf, lecture14_notes.pdf
|
| Mon Nov 02 |
Lecture 15: topic models, Markov and Hidden Markov Models
|
lecture15_slides.pdf
|
| Wed Nov 04 |
Lecture 16: Hidden Markov Models cont'd
|
lecture16_slides.pdf
|
| Mon Nov 09 |
Lecture 17: Bayesian networks
|
lecture17_slides.pdf, lecture17_notes.pdf
|
| Mon Nov 16 |
Lecture 18: Learning Bayesian networks
|
lecture18_slides.pdf, lecture18_notes.pdf
|
| Wed Nov 18 |
Lecture 19: Markov Random Fields, Factor graphs, inference
|
lecture19_slides.pdf, lecture19_notes.pdf
|
| Mon Nov 23 |
Lecture 20: Belief propagation, learning MRFs
|
lecture20_slides.pdf
|
| Wed Nov 25 |
Lecture 21: Inference, junction trees
|
|
| Mon Nov 30 |
Lecture 22: conditional models, structured prediction
|
|
| Wed Dec 02 |
Lecture 23: Learning conditional models
|
|
| Mon Dec 07 |
FINAL EXAM
: open book
|
| Wed Dec 09 |
Lecture 24: current topics in machine learning, wrap-up
|
|