1. Be able to formulate machine learning problems corresponding to
2. Understand a range of machine learning algorithms along with
their strengths and weaknesses.
3. Understand the basic theory underlying machine learning.
4. Be able to aply machine learning algorithms to solve problems of
5. Be able to read current research papers and understand the issues raised by current research.
There will be two exams:
The assignments in this course will consist of written problem sets and
experimental assignments. The written homework problems are designed to
help students build analytical skills required to carry out research
related to machine learning. Note that the written assignment will not
be graded based on correctness. Rather the instructor will record the
number of problems that were "completed" (either correctly or
incorrectly). Completing a
problems requires demonstrating a non-trivial attempt at solving the
problem. The judgment of whether a problem was "completed" is left to
Collaboration on assignments is permitted; copying of
solutions or code is not. The work you hand in should be your
own. Students should indicate on their homework, the names of all
collaborators. While some students find studying together to be quite
beneficial and enjoyable, I strongly encourage you to attempt to solve
homework problems on your own first, as this is the only way to ensure
that you have mastered the material. Generating solutions is much
different than verifying solutions.