Course Description This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
We strongly encourage students to form study groups. Students may discuss and work on homework problems
in groups. However, each student must write down the solution independently, and without referring to
written notes from the joint session. Students submitting in a pair act as one unit - they may share
resources (such as notes) with each other and write the solutions together. Note that both of the two
students should fully understand all the answers in their submission, even though only one of them needs
to write up a solution to a question. In other words, each student must understand the solution well
enough
in order to reconstruct it by him/herself. In addition, each student should write on the problem
set the set of people with whom s/he collaborated.
Further, since we occasionally reuse problem set questions from previous years, we expect students not to
copy, refer to, or look at the solutions in preparing their answers. It is an honor code violation to
intentionally refer to a previous year's solutions. This applies both to the official solutions and
to solutions that you or someone else may have written up in a previous year.