Machine Learning

Logistics

Location ECCS 1B12
Time Tue/Thu 9:30 - 10:45
Webpage http://cs.colorado.edu/~jbg/teaching/CSCI_5622/
Mailing List https://piazza.com/class#fall2015/csci5622
Required Text Machine Learning: A Probabilistic Perspective (Murphy)
Suggested Text Foundations of Machine Learning (FML)
Syllabus https://docs.google.com/document/d/1JyKb5JolsvttYb5jem1CgNskDUT79_yYVd5zKTTTg9s
Video Lectures D2L (Click on "Lecture Access" in the top left)
Grades and Submission Moodle

People

Professor

Jordan Boyd-Graber
ECCS 111B
Office Hours (ECCS Lobby, outside ECCS 111): Starting 8. September, Tuesday 8:00 - 9:00 and by appointment

Grader

  1. Manjhunath Ravi (Manjhunath.Ravi@colorado.edu)
  2. Nora Connor (Nora.Connor@colorado.edu)
  3. Mahnaz Roshanaei (Mahnaz.Roshanaei@colorado.edu)

    Schedule

    Date In-Class Topic Assignment Due Lecture
    Tue 25. Aug 1. Machine Learning as a Black Box [Video AB] [PDF ABC]
    Readings:
    • Murphy 1
    • (Assumed background) Murphy Chapter 2
    Thu 27. Aug 2. Logistic Regression [Video AB] [PDF ABC]
    Readings:
    • Murphy 3, 8 - 8.4
    • (Assumed Background) Murphy Chapter 2
    Optional:
    Tue 1. Sep 3. Stochastic Gradient Optimization for Logistic Regression [Video A] [PDF AB]
    Readings:
    Thu 3. Sep CLASS CANCELLED
    Fri 4. Sep Homework 1 Due K Nearest Neighbors
    Tue 8. Sep 4. Feature Engineering [Video A] [Script] [PDF, data]
    Readings:
    Thu 10. Sep 5. PAC Learnability [Video A] [PDF AB]
    Readings:
    Fri 11. Sep Homework 2 Due Logistic Regression
    Tue 15. Sep 6. VC / Rademacher Complexity [Video A] [PDF AB]
    Readings:
    Thu 17. Sep 7. Support Vector Machines [Video A] [PDF AB]
    Readings:
    Fri 18. Sep Homework 3 Due Feature Engineering
    Tue 22. Sep 8. Dual/Slack SVMs [Video A] [PDF AB]
    Readings:
    Thu 24. Sep 9. Kernel Methods [Video AB] [PDF ABC] [Script]
    Readings:
    • Murphy 14
    Tue 29. Sep 10. Boosting [Video A] [PDF AB]
    Readings:
    • Murphy 16-16.4
    Thu 1. Oct 11. Regression [Video A] [PDF AB] [Data]
    Readings:
    • Murphy 7
    Fri 2. Oct Homework 4 Due Learnability
    Tue 6. Oct 12. Sparse Regression [Video A] [PDF A]
    Readings:
    Thu 8. Oct CLASS CANCELLED
    Fri 9. Oct Homework 5 Due SVM
    Tue 13. Oct CLASS CANCELLED
    Thu 15. Oct 13. Ranking and Multi-class Classification [Video AB] [PDF ABC] [Data Train/Test]
    Readings: Optional:
    Fri 16. Oct Homework 6 Due Boosting
    Tue 20. Oct Review / Catchup
    Thu 22. Oct Midterm
    Tue 27. Oct 14. Deep Learning I [No video—guest lecture by Mike Mozer] [Video A] [PDF A]
    Readings:
    Thu 29. Oct 15. Deep Learning II [No video—guest lecture by Mike Mozer] [Video A] [PDF A]
    Readings:
    • Murphy 28
    Fri 30. Oct Project Milestone Project Proposal
    Tue 3. Nov 16. K-Means / Mixture Models [Video A] [PDF AB]
    Readings:
    • CIML chapters 13.
    • Murphy 11.1-11.4
    Thu 5. Nov 17. Dirichlet Process / Gibbs Sampling [Video A] [PDF AB]
    Readings:
    • Murphy 24.1-24.2
    • Murphy 25.1-25.2
    Optional:
    Tue 10. Nov 18. Topic Models [Video A] [PDF AB]
    Readings:
    • Murphy 27.1-27.3.4
    Thu 12. Nov 19. Variational Inference [Video A] [PDF AB]
    Readings:
    • Murphy 21
    • Murphy 27.3.5-27.4
    Fri 13. Nov Project Milestone First Deliverable
    Tue 17. Nov 20. Hidden Markov Models [Video A] [PDF AB]
    Readings:
    • Murphy 17
    Thu 19. Nov 21. Online Learning and Structured Perceptron [Video A] [PDF AB]
    Readings:
    Tue 1. Dec 22. Reinforcement Learning [Video A] [PDF AB]
    Readings:
    Thu 3. Dec 23. Social History of Machine Learning [No video—in-class discussion]
    Optional:
    Fri 4. Dec Homework 7 Due Variational Bayes
    Tue 8. Dec Final Project Workshop
    Thu 10. Dec Final Exam Distributed
    Tue 15. Dec, 1:30 Final Exam / Oral Final Report