CS 194-10, Fall 2011: Introduction to Machine Learning
Lecture slides, notes
Slides and notes may only be available for a subset of lectures. The lecture itself is the best source of information.
Week 1 (8/25 only):
Week 2 (8/30, 9/1):
- Lecture continued from the preceding week's slides.
- Linear regression was covered on the blackboard.
Week 3 (9/6, 9/8):
- Slides for Machine learning methodology: Overfitting, regularization, and all that (pdf)
- Slides for Linear classification (pdf)
Week 4 (9/13, 9/15):
- Slides for Logistic Regression (pdf)
- Slides for Kernels for Classication and Regression (pdf)
Week 5 (9/20, 9/22):
- Slides for Decision Tree Learning (pdf)
- Slides for Ensemble Methods (pdf) (mainly an "animation" of boosting)
Week 6 (9/27, 9/29):
- Slides for Instance-Based Methods (pdf) (cross-validating "k" in k-NN)
Week 7 (10/4, 10/6):
Week 8 (10/11, 10/13):
- Slides for Learning with probabilities (pdf)
Week 9 (10/18, 10/20):
- Slides for Gaussian discriminants; logistic regression revisited (pdf)
- Slides for Bayesian parameter estimation (images only) (pdf)
Week 10 (10/25, 10/27):
- Slides for Bayesian linear regression (images only) (pdf)
- Slides for Density estimation (images only) (pdf)
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Week 11 (11/1, 11/3):
- Slides for K-means, EM (images only) (pdf)
- Notes for The EM algorithm (1998) (pdf)
- Slides for Bayes nets: representation (pdf)
Week 12 (11/8, 11/10):
- Slides for Bayes nets: inference and learning (pdf)
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Week 13 (11/15, 11/17):
- Slides for Temporal models (n-grams, AR models) (pdf)
- Slides for Temporal probability models (HMMs, DBNs) (pdf)
Week 14 (11/22 only):
Week 15 (11/29, 12/1):