This introductory course on machine learning will give an overview of many techniques and algorithms in machine learning, beginning with topics such as simple perceptrons and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered.
Here is a partial list of concepts, methods and tools dealt with in
this course:
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Tue/Thu 2:30-4pm in E37-212.
Wed 11am-12pm in 38-166
Fri 3-4pm in 36-156
Each problem set will be graded by a group of students with the guidance of your TA. Each problem set will be graded in a single grading session, usually on the first Monday after it is due, starting at 5pm. Every student is required to participate in one grading session. You should sign up for grading by contacting the TA, by email or in person; doing it early increases the chances of getting the preferred grading schedule. Students who do not register for grading by the third week of the course, will be assigned to a problem set by us.
If you drop the class after signing up for a grading session, please be sure to let us know so we can keep track of students available for grading. If you add the class durring the term, please remember to sign up for grading.
The projects are due on Wednesday, December 11th. The short proposal should be turned in on or before October 29th.
The projects can be literature reviews, theoretical derivations or analyses, applications of machine learning methods to problems you are interested in, or something else.
You will not be able to find all the course material in the text nor do we plan to go through the chapters in order or in full. You are responsible for the material covered in lectures and problem sets, as well as in the chapters/sections of the text specifically indicated. The weekly recitations/tutorials will be helpful in understanding the material and solving the homework problems.