10-601B, Fall 2016
School of Computer Science
Carnegie Mellon University
This schedule is tentative and subject to change. Please check back often.
Recorded lectures can be found here
Note: you will need to log in with your Andrew ID
| Date | Lecture | Readings | Homework Released | Homework Due |
|---|---|---|---|---|
| Mon, 29-Aug | 1. Course Overview & Decision Tree Learning [Slides], [More] | |||
| Wed, 31-Aug | 2. Decision Tree Learning [Slides] Intro to Probability [Slides], [PPT], [More] |
Mitchell Chapter 1, 2; 6.1-6.3; Murphy Chapter 2; Bishop Chapter 1, 2 | HW1: Background Test | |
| Mon, 5-Sep | (No Class: Labor Day). | |||
| Wed, 7-Sep | 3. The Naive Bayes algorithm [Slides], [PPT], [More] | Mitchell 6.1-6.10; Murphy 3 | HW2: MLE & Naive Bayes | HW 1 |
| Mon, 12-Sep | 4. Logistic Regression [Slides], [PPT], [More] | Murphy 8.1-3, 8.6; Maximum Likelihood, Logistic Regression, and Stochastic Gradient Training | ||
| Wed, 14-Sep | 5. Linear Regression [Slides], [PPT], [More] | Mitchell 4.1-4.3; Murphy: 7.1-7.3, 7.5.1; Bishop 3.1 | HW3: Linear & Logistic Regression | HW 2- 9/16 |
| Mon, 19-Sep | 6. Linear Regression (cont'd) [Slides], [PPT] Perceptrons and Large Margin [PPT Slides], [PDF Slides], [More] |
Mitchell 4.4.1-4.1.2; Bishop 4.1.7 | ||
| Wed, 21-Sep | 7. Kernels [Slides], [More] | Bishop 6.1-6.2 | ||
| Mon, 26-Sep | 8. Support Vector Machines (SVMs) & Kernelizing SVMs [Slides], [More] | Bishop 7.1; Murphy 14.5 | HW4: SVMs & Kernels | HW 3 |
| Wed, 28-Sep | 9. Generalization and Overfitting: Sample Complexity Results [Slides], [More] | Mitchell Chapter 7 | ||
| Mon, 3-Oct | 10. Generalization, Overfitting, and Model Selection; Sample Complexity Results [Slides], [More] | |||
| Wed, 5-Oct | 11. Model Selection [Slides] Midterm Review [Slides], [PPT] |
HW 4 | ||
| Mon, 10-Oct | 12. Midterm exam | |||
| Wed, 12-Oct | 13. Clustering [Slides], [PPT], [More] | Murphy 25.5 | ||
| Mon, 17-Oct | 14. Clustering 2 [Slides], [PPT] PCA and dimension reduction [Slides], [PPT], [More] |
Bishop 12.1, 12.3 | ||
| Wed, 19-Oct | 15. Neural Networks [Slides], [PPT], [Whiteboard Notes], [More] | Mitchell Chapter 4, Bishop Chapter 5 | ||
| Mon, 24-Oct | 16. Deep Learning 1 [Slides], [PPT], [More] | Neural Networks and Deep Learning (online book by Michael Nielsen) | HW5: Unsupervised Learning | |
| Wed, 26-Oct | 17. Deep Learning 2 [Slides], [PPT], [More] | |||
| Mon, 31-Oct | 18. Boosting [Slides], [PPT], [More] | The Boosting Approach to Machine Learning: An Overview by Robert E. Schapire | ||
| Wed, 2-Nov | 19. Active Learning [Slides], [PPT], [More] | Two Faces of Active Learning by Sanjoy Dasgupta | HW6: Deep Learning | HW 5 |
| Mon, 7-Nov | 20. Semi-Supervised Learning [Slides], [More] | Introduction to Semi-Supervised Learning by Jerry Zhu, Chapters 1-3, 5 (available free on CMU network) | ||
| Wed, 9-Nov | 21. Graphical Models 1 [Slides], [PPT], [Whiteboard Notes] | Mitchell 6.11; Murphy Chapter 10; Bishop 8.1 and 8.2.2; Russell and Norvig Chapter 15 | ||
| Mon, 14-Nov | 22. Graphical Models 2 [Slides], [PPT] | |||
| Wed, 16-Nov | 23. HMMs and CRFs [Slides], [PPT], [Whiteboard Notes] | Bishop 13.1-13.2; An Introduction to Conditional Random Fields by Sutton and McCallum | ||
| Mon, 21-Nov | 24. Expectation Maximization, [Slides], [PPT], [Whiteboard Notes] | Mitchell 6.2; Murphy 11.4.1, 11.4.2, 11.4.4 | HW7: Graphical Models & Active Learning | HW 6 |
| Wed, 23-Nov | (No Class: Thanksgiving). | |||
| Mon, 28-Nov | 25. Reinforcement Learning [Slides] | Mitchell Chapter 13; Reinforcement Learning, A Survey by Kaelbling, et al | ||
| Wed, 30-Nov | 26. Matrix Factorization and Collaborative Filtering [Slides], [PPT], [Whiteboard Notes] | Matrix Factorization Techniques for Recommender Systems by Koren, Bell, and Volinsky; Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent by Gemulla et al. | ||
| Mon, 5-Dec | 27. Review [Slides], [PPT] | HW 7 | ||
| Wed, 7-Dec | 28. In-class final exam |