­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.


       Yanjun (Jane) Qi, Rice Hall 503


Course Website :

       Collab page

       Corrections or comments to yq2h@virginia.edu



Tentative schedule:




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


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


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


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 (required): Elements of Statistical Learning, Hastie, Tibshirani and Friedman.




Question from each student before each class:

       For each class' content, please submit at least 3 questions.

       TA will grade the questions as assignments.



       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).