Video links

The lecture recordings of the two courses in the course links, arranged by chapter. The reading plan gives the matching slides and notes and says which parts of a lecture to watch.

Video or playlist	Link	Notes
Kilian Weinberger, Machine Learning for Intelligent Systems (Cornell CS4780, Spring 2017)	Playlist	The recordings used with the Fall 2018 topic notes. Recording numbers differ from topic numbers; the table below uses recording numbers.
Tom Mitchell and Maria-Florina Balcan, Machine Learning (CMU 10-601, Spring 2015)	Lecture schedule	The schedule lists each lecture with its slides; the recordings are linked individually below.

By chapter

Chapter	CMU 10-601 lectures	Cornell CS4780 recordings
1. Learning Problems and Nearest Neighbors	Jan 12: Introduction and decision trees	Video 1 · Video 2 Video 3 · Video 4
2. The Perceptron and Linear Separation	—	Video 5 · Video 6
3. Linear Regression and Regularization	Feb 4: Linear regression and the generative–discriminative comparison	Video 13
4. Generative Classifiers	Jan 21: Probability and estimation Jan 26: Naive Bayes Jan 28: Gaussian naive Bayes	—
5. Logistic Regression and Probabilistic Prediction	Feb 2: Logistic regression	Video 11
6. Losses, Model Selection, and Evaluation	—	Video 15 · Video 16 Video 19 · Video 20 Video 21
7. Statistical Learning Theory	Feb 9: Learning theory I Feb 11: Learning theory II Feb 16: Learning theory III	—
8. Support Vector Machines and Kernels	Mar 23: Margins and kernels Mar 25: Support vector machines	Video 22 · Video 23 Video 24 · Video 25
9. Decision and Regression Trees	Jan 14: Decision trees and overfitting	Video 28 · Video 29
10. Bagging and Random Forests	—	Video 30 · Video 31
11. Boosting	Mar 16: Boosting Mar 18: AdaBoost guarantees and margins	Video 32 · Video 33 · Video 34
12. Principal Components and Dimensionality Reduction	Apr 8: PCA and kernel PCA	—
13. Clustering	Apr 6: Partitional and hierarchical clustering	—
14. Gaussian Mixtures and Expectation Maximization	Feb 25: Learning with hidden variables and EM Mar 4: Gaussian mixtures, EM and clustering	—
15. Gaussian Processes	—	Video 26 · Video 27
16. Semi-Supervised and Active Learning	Mar 30: Semi-supervised learning Apr 1: Active learning	—
17. Smoothing, Density Estimation, and Basis Expansions	—	Video 27, later portion: \(k\)-d trees and ball trees

Some lectures cover more than one chapter. The CMU lecture of 12 January introduces decision trees (chapter 9) as the first example of learning; the lecture of 4 February compares generative and discriminative classifiers (chapters 4–5); the boosting lecture of 18 March returns to the perceptron (chapter 2); and the SVM lecture of 25 March includes semi-supervised learning (chapter 16). Cornell video 21, from the model-selection lecture, also introduces kernels (chapter 8).