CSC 411F/2515F -- Fall 2012
Lectures: Monday, Wednesday 3-4
Tutorials: Friday 3-4
Pre-requisites: You should understand basic probability and statistics, and college-level algebra and calculus. For example it is expected that you know about standard probability distributions (Gaussians, Poisson), and also how to calculate derivatives. Knowledge of linear algebra is also expected. For the programming assignments, you should have some background in programming, and it would be helpful if you know Matlab. Some introductory material for Matlab will be available on the course website as well as in the first tutorial.
Readings: The recommended textbook for this course is: Introduction to Machine Learning by Ethem Alpaydin. Readings from other books, such as Bishop's Pattern Recognition and Machine Learning, will also be recommended.
Course schedule: The schedule of lectures, readings, and assignments will be available shortly.
Course requirements and grading: The format of the class will be lecture, with some discussion. I strongly encourage interaction and questions. There are assigned readings for each lecture that are intended to prepare you to participate in the class discussion for that day.
The grading in the class will be divided up as follows:
Homework assignments: The best way to learn about a machine learning method is to program it yourself and experiment with it. So the assignments will generally involve implementing machine learning algorithms, and experimentation to test your algorithms on some data. You will be asked to summarize your work, and analyze the results, in brief (3-4 page) write ups. The implementations must be done in Matlab, but prior knowledge of Matlab is not required. A brief tutorial on Matlab is included here. You may also use Octave.
Collaboration on the assignments is not allowed. Each student is responsible for his or her own work. Discussion of assignments and programs should be limited to clarification of the handout itself, and should not involve any sharing of pseudocode or code or simulation results. Violation of this policy is grounds for a semester grade of F, in accordance with university regulations.
The schedule of assignments is included in the syllabus. Assignments are due at the beginning of class/tutorial on the due date. Because they may be discussed in class that day, it is important that you have completed them by that day. Assignments handed in late but before 5 pm of that day will be penalized by 5% (i.e., total points multiplied by 0.95); a late penalty of 10% per day will be assessed thereafter. Extensions will be granted only in special situations, and you will need a Student Medical Certificate or a written request approved by the instructor at least one week before the due date.
For one of the assignments, we will have a bake-off: a competition between machine learning algorithms. I will give everyone some data for training a machine learning system, and you will try to develop the best method. We will then determine which system performs best on some unseen test data.
Exams: There will be a mid-term in class on October 23rd, which will be a closed book exam on all material covered up to that point in the lectures, tutorials, required readings, and assignments.
The final will not be cumulative, except insofar as concepts from the first half of the semester are essential for understanding the later material.
Attendance: I expect students to attend all classes, and all tutorials. This is especially important because I will cover material in class that is not included in the textbook. Also, the tutorials will not only be for review and answering questions, but new material will also be covered.