Introduction to Machine Learning

10-601B, Fall 2016
School of Computer Science
Carnegie Mellon University

Important Notes

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

Tentative Schedule

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