Date Class Period & Lecture Topic Reading Assignment
Jan 04 1. Syllabus, policies, business
Jan 06 No meeting (ICCCX) Topic Suggestions
Jan 11 2. Sparse coding Sparse Coding papers
Jan 13 3. Sparse coding
Jan 18 No meeting (holiday)
Jan 22 4. Sparse coding (Langrange/Dual)
Jan 25 5. Deep networks Deep Networks papers
Jan 27 6. Deep networks
Feb 01 7. Markov random fields
Feb 03 8. Markov random fields Markov Random Fields papers
Feb 08 No meeting (NSF)
Feb 10 9. Snow day
Feb 12 10. Structure SVMs Structured SVM papers
Feb 15 No meeting (holiday)
Feb 16 11.Structure SVMs
Feb 17 12.Feature Selection (Rob) Feature Selection papers
Feb 22 13. Feature Selection (Rob)
Midterm (Feb 23–26, Take Home)
Feb 24 14. No meeting (midterm)
Mar 01 15. SVM Model Selection (Beau) SVM Model Selection papers
Mar 03 16. SVM Model Selection (Beau)
Mar 08 17. NeuroEvolution (Dave) NeuroEvolution papers
Mar 10 18. NeuroEvolution (Dave)
Mar 15 19. POMDP (Mike) POMDP papers
Mar 17 20. POMDP (Mike)
Mar 22 21. Deep Transfer (Spencer) Deep Transfer papers
Mar 24 22. Deep Transfer (Spencer)
Mar 29 23. Hierarchical Reinforcement Learning (Brian) Hierarchical Reinforcement Learning papers
Mar 31 24. Hierarchical Reinforcement Learning (Brian)
Apr 05 25. Active Learning (Sabra) Active Learning papers
Apr 07 26. Active Learning (Sabra)
Apr 12 27. Culinary Search Culinary Search papers
Apr 17 Final (7:00–10:00am in class)