Course Outline for CS485, Winter 2012

University of Waterloo

Term and Year of Offering: Winter 2012

Course Number and Title: CS485/685, Machine Learning

Website: http://www.student.cs.uwaterloo.ca/~cs485

Comp Sec	Camp Loc	Time Days/Date	Bldg Room	Instructor
LEC 001	UW U	01:00-02:20TTh	MC 4020	Poupart,Pascal

Instructor's Name	Office Location	Contact	Office Hours
Pascal Poupart	DC2514	ppoupart@cs.uwaterloo.ca	We 8:30-10:30

TA's Name	Office Location	Contact	Office Hours
Ricardo Salmon		rsalmon@cs.uwaterloo.ca
Ruth Urner		rurner@cs.uwaterloo.ca

Course Description:

Machine learning is a fast growing topic for both commercial applications and academic research. It addresses the issue of how computers can "learn", that is, how the process of drawing useful conclusions from massive data sets can be automated. Machine learning plays a central role in a wide range of applications emerging from the need to process data sets whose sizes and complexities are beyond the ability of humans to handle. The course will cover the theoretical foundations and the design of algorithms for machine learning. It draws from several established mathematical areas including statistics, geometry, combinatorics, and computational complexity.

Course Objectives:

At the end of the course, students should have the ability to:

Analyze a dataset with machine learning tools
Design, implement and test some machine learning algorithms
Analyze the sample and computational complexity of machine learning algorithms

Course Overview:

The following topics will covered:

Introduction to machine learning
Theoretical foundation (e.g. learning theory and Bayesian learning)
Classification
Regression
Specific models/algorithms (e.g., nearest neighbors, decision trees, linear/nonlinear separators and regression, neural networks, kernel methods, support vector machines, sequential data modeling)

Required text:

Readings in the textbooks are assigned for every lecture in the course schedule.

Christopher Bishop, Pattern Recognition and Machine Learning (2006)
Shai Ben-David & Shai Shalev-Shwartz, Machine Learning: From Theoretical Principles to Practical Algorithms (in preparation)

Evaluation:

The grading scheme for the course is as follows.

CS485:

Assignments (4): 50% (12.5% each)
Final Exam: 50%
Optional project: 5% bonus
NB: you must pass the final exam to pass the course

CS685 (graduate students only):

Assignments (4): 30% (7.5% each)
Final Exam: 40%
Project: 30%
NB: you must pass the final exam to pass the course

Assignments:

There will be four assignments given the course. Each assignment will have a theoretical part and a programming part. Assignments are done individually (i.e., no team). You are free to program in the language of your choice, however Matlab is recommended since it provides a convenient high-level programming environment for matrix operations. If you decide to program in Matlab, the IST group maintains a nice set of online references for Matlab including a tutorial.

The approximate out and due dates are:

A1: out Jan 10, due Jan 26
A2: out Jan 26, due Feb 14
A3: out Feb 14, due March 6
A4: out March 6, due March 22

Tests:

There is no midterm.

There will be a final examination of 2.5 hours to be scheduled by the registrar.

Rules for Group Work:

Assignments must be done individually. Projects can be done in groups of up to 3 people for undergraduate students, but must be done individually for graduate students.

Indication of how late submission of assignments and missed assignments will be treated

On the due date of an assignment, the work done to date should be submitted at the beginning of class; further material may be submitted for half credit within 24 hours. Assignments submitted more than 24 hours late will not be marked.

Indication of where students are to submit assignments and pick up marked assignments

Assignments must be submitted in class or in the drop off box. Marked assignments will be returned in class or can be picked up later from Jessica Miranda's office (DC3516).

Academic Integrity: In order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility. [Check www.uwaterloo.ca/academicintegrity/ for more information.]

Grievance: A student who believes that a decision affecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70, Student Petitions and Grievances, Section 4, www.adm.uwaterloo.ca/infosec/Policies/policy70.htm. When in doubt please be certain to contact the department's administrative assistant who will provide further assistance.

Discipline: A student is expected to know what constitutes academic integrity [check www.uwaterloo.ca/academicintegrity/] to avoid committing an academic offence, and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offence, or who needs help in learning how to avoid offences (e.g., plagiarism, cheating) or about 'rules' for group work/collaboration should seek guidance from the course instructor, academic advisor, or the undergraduate Associate Dean. For information on categories of offences and types of penalties, students should refer to Policy 71, Student Discipline, www.adm.uwaterloo.ca/infosec/Policies/policy71.htm. For typical penalties check Guidelines for the Assessment of Penalties, www.adm.uwaterloo.ca/infosec/guidelines/penaltyguidelines.htm.

Appeals: A decision made or penalty imposed under Policy 70 (Student Petitions and Grievances) (other than a petition) or Policy 71 (Student Discipline) may be appealed if there is a ground. A student who believes he/she has a ground for an appeal should refer to Policy 72 (Student Appeals) www.adm.uwaterloo.ca/infosec/Policies/policy72.htm.

Note for Students with Disabilities: The Office for persons with Disabilities (OPD), located in Needles Hall, Room 1132, collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with the OPD at the beginning of each academic term.