Machine Learning


Location ESJ 2212
Time Mon/Wed 15:30 - 16:45
Mailing List Piazza
Required Text Understanding Machine Learning: From Theory to Algorithms (UML)
Suggested Text Foundations of Machine Learning (FML)
Suggested Text Machine Learning: A Probabilistic Perspective (Murphy)
Video Lectures YouTube
Grades and Submission ELMS



Jordan Boyd-Graber
AVW 3155
Office Hours: Starting 30. August, Wed 14:00 - 15:00 and by appointment


  1. Fenfei Guo (Despite Colorado URL, will be PhD student at UMD in Fall) Office hours Thu 14:00 - 16:00 in AVW 3164


    Date In-Class Topic Assignment Due Lecture
    Mon 28. Aug 1. Machine Learning as a Black Box [Video A B C] [PDF A B C]
    Wed 30. Aug 2. Logistic Regression [Video A B C] [PDF A B C]
    Readings: Optional:
    Mon 4. Sep Labor Day
    Wed 6. Sep 3. Stochastic Gradient Optimization for Logistic Regression [Video A B] [PDF A B C]
    Fri 8. Sep Homework 1 Due K Nearest Neighbors
    Mon 11. Sep LAB DAY for HW2
    Wed 13. Sep 4. Feature Engineering [Video A B] [POS Script] [data] [Slides]
    Fri 15. Sep Homework 2 Due Logistic Regression
    Mon 18. Sep 5. PAC Learnability [Video A B] [PDF A B]
    Readings: (Choose one)
    Wed 20. Sep 6. VC / Rademacher Complexity [Video A B] [PDF A] [PDF B] [PDF C]
    Readings: (Choose one)
    • FML 3
    • UML 6, 26
    Mon 25. Sep Class Cancelled
    Wed 27. Sep 8. Support Vector Machines [Video A B C D] [PDF A B C D]
    Readings: (Choose One)
    • UML 15-16
    • FML 4-5
    Fri 29. Sep Homework 3 Due Feature Engineering
    Mon 2. Oct 9. Boosting [Video A B] [PDF A B]
    Readings: (Choose one)
    • UML 10
    • FML 6
    Wed 4. Oct 10. Regression [Video A B] [PDF AB] [Data]
    Readings: (Choose one)
    • FML 10
    • UML 9.2
    Fri 6. Oct Homework 4 Due Learnability
    Mon 9. Oct 11. Structured Perceptron [Video A B C D E F] [PDF A B C D]
    Readings: (Choose one)
    Wed 11. Oct 12. Loss Functions and Multilayer Backprop [PDF A B C] [Video A B1 B2 C]
    Readings: (Choose one)
    • UML 20
    Fri 13. Oct Homework 5 Due SVM
    Mon 16. Oct Review / Catchup
    Wed 18. Oct Midterm
    Fri 20. Oct Project Milestone Project Proposal
    Mon 23. Oct 13. Representation Learning [Video A B C] [PDF A B C D]
    Wed 25. Oct 14. K-Means / Mixture Models [Video A] [PDF A B]
    • CIML chapter 15.
    Mon 30. Oct 15. Dirichlet Process / Gibbs Sampling [Video A] [PDF A B]
    Readings: Optional:
    Wed 1. Nov 16. Topic Models [Video A] [PDF A B]
    Readings: Optional:
    Mon 6. Nov 17. Variational Inference [Video A] [PDF A B]
    Wed 8. Nov 18. Variational Autoencoders and Generative Adversarial Networks [PDF A B C D]
    Fri 10. Nov Project Milestone First Deliverable
    Mon 13. Nov 19. Memory Models (LSTMs, GRUs) [Video A B] [PDF A B C D]
    Wed 15. Nov 20. Reinforcement Learning [Video A B C] [PDF A B C D]
    Fri 17. Nov Homework 6 Due Variational
    Mon 20. Nov 21. Ranking, Regret, and Multiclass [PDF A B C D]
    Wed 22. Nov Thanksgiving
    Mon 27. Nov 22. Fairness, Acountability, and Transparency
    Wed 29. Nov 23. Will AI kill and/or enslave humanity?
    Mon 4. Dec 24. The Culture of Machine Learning [Unflipped]
    Wed 6. Dec Class Cancelled (NIPS): Come to class time to review for final
    Fri 8. Dec Homework 7 Due Deep Learning
    Mon 11. Dec Midterm
    Fri 15. Dec, 1:30 Final Presentations Final Report