Machine
Learning Seminar Course,
CS6501
- 02
Department of Computer Science, University of Virginia
Fall
2013
Course Logistics
Using
a collaborative learning style, this course aims
to enhance the CS graduate students' understanding of
statistical machine learning methods.
Class Description: The course will take the
form of a seminar instead of lectures by the instructor. We will go through the
textbook, one chapter per class except that a heavy chapter may be split into
two or three classes. Each class starts by summarizing questions from all the
participants about the current chapter, followed by a presentation (lecture) on
that chapter, and then classroom discussions about collected and new questions.
Students will be grouped into teams of two or three persons; each team is
assigned to two chapters or three). Each team will analyze, deliver a
lecture and lead the classroom discussions. All the students are required to
read every chapter before it is discussed in a class, and sent their questions
through Collab in advance.
Class Time: Tuesdays & Thursdays 11:00am-12:15pm , starting on
Tuesday August 27th, 2013
Class Location: Rice Hall 120
TA: Tung Dao ( thd7wk@virginia.edu) will
hold the office hours from 4-5pm on every Tuesday.
Instructors:
•
Yanjun (Jane)
Qi, Rice Hall 503
Course Website :
•
Collab page
•
Corrections or comments
to yq2h@virginia.edu
Tentative
schedule:
Date |
Content |
TU Aug 27 |
Overview of class |
TR Aug 29 |
Overview of logistics |
TU Sept 3 |
Ch 1-2. Introduction; Supervised
Learning |
TR Sept 5 |
Ch 3. Linear Methods for Regression (part 1) 3.1-3.4.4 |
TU Sep 10 |
Ch 3. Linear Methods for Regression (part 2) 3.5-3.9 (no 3.7) |
TR Sep 12 |
Ch 4. Linear Methods for
Classification (4.1-4.4) |
TU Sep 17 |
Ch 5. Basis Expansion and Regularization
(5.1 to 5.5) |
TR Sep 19 |
Ch 6. Kernel Smoothing Methods |
TU Sep 24 |
Ch 7. Model Assessment and
Selection (7.1 – 7.10) |
TR Sep 26 |
Ch 8. Model Inference and Averaging (8.1-8.7) |
TU Oct 1 |
Ch 9. Additive Models, Trees, and
Related Methods (9.1-9.4) |
TR Oct 3 |
Special topics / Review/ Sample
Projects |
TU Oct 8 |
Ch 10. Boosting and Additive Trees (part 1: 10.1 – 10.9) |
TR Oct 10 |
Ch 10. Boosting and Additive Trees (part 2: 10.10 – 10.14) |
TR Oct 17 |
Ch 11. Neural Networks |
TU Oct 22 |
Ch 12. Support Vector Machines
& Flexible Discriminants (part 1: 12.1-12.3.8 + Ch 4.5 ) |
TR Oct 24 |
READING DAY |
TU Oct 29 |
Ch 13. Prototype Methods and Nearest-Neighbors |
TR Oct 31 |
Ch 14. Unsupervised Learning (part 1: 14.1 and 14.3 )
+ Ch 12.5 - note: no 14.2 in this class ! |
TU Nov 5 |
Ch 14. Unsupervised Learning (part 2: 14.5, 14.6, 14.7 ) |
TR Nov 7 |
Ch 14. Unsupervised Learning (part 3: 14.2, 14.4,
14.8-14.10 ) |
TU Nov 12 |
Ch 15. Random Forest + Ch 8.7,8.8,8.9 |
TR Nov 14 |
Ch 16. Ensemble Learning |
TU Nov 19 |
Ch 17. Undirected Graphical Model |
TR Nov 21 |
Ch 18. High-Dimensional Problem (Part 1: 18.1-18.4.2) |
TU Nov 26 |
Ch 18. High-Dimensional Problem (Part 2: 18.5-18.7) |
TU Dec 3 (Rice 120) |
Project Presentation by each
student ( 7 minutes / student) |
WED Dec 4 (Rice 120) |
Project Presentation by each
student ( 7 minutes / student) |
THUR Dec 5 (Rice 120) (11am - 12:30pm) |
Project Presentation by each
student ( 7 minutes / student) |
Mon Dec 9 |
|
Textbook:
•
Textbook (required): Elements of Statistical Learning,
Hastie, Tibshirani and Friedman.
•
www.stanford.edu/~hastie/local.ftp/Springer/OLD//ESLII_print4.pdf
Question from each student before each class:
•
For each class' content, please
submit at least 3 questions.
•
TA will grade the questions as
assignments.
Grading:
•
No exams in this course.
•
Sit-in: No. This course is for registered
students only.
•
2 pages write-up required for
permitted absence of a class
•
•
Final grades will be
based on.
•
–15% for participation in class;
•
–15% for the quality of the questions
and discussion in class;
•
–40% for the quality of the seminar
presentations;
•
–30% for the quality of written
project reports by each individual in the second half of the semester
Course Project Report:
•
By the end of this
semester, each student is required to submit a project report (through three
incremental phases).
•
This report (IEEE
conference paper submission format, double column, 4 pages min limit, 10 pages
max limit).
•
Template could be
obtained from:
http://www.ieee.org/conferences_events/conferences/publishing/templates.html
•
It aims to motivate
students applying statistical machine learning on their research projects, or
some research problem they are interested with.
•
At the same time, on the
last three classes, each student is required to make an in-class 7 minutes
presentation about their course proposal.
•
The project proposal is
due around the mid of Oct (2 pages length requirement).
•
The mid-phase project
report is due in the mid of Nov.
•
The final draft of this
proposal is due at the end of this semester.
•
Depending on the
student’s level of ML, your report could be either a proposal or a full project
report (extra credits will be added if full project report).