This class is the graduate version of 6.036 Introduction to Machine Learning. Quoting from the description of 6.036:
Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. In this course, you will learn about principles and algorithms for turning training data into effective automated predictions. We will cover concepts such as representation, over-fitting, regularization, and generalization; topics such as clustering, classification, recommender problems, probabilistic modeling, reinforcement learning; and methods such as on-line algorithms, support vector machines, neural networks/deep learning, hidden Markov models, and Bayesian networks.
The graduate version (6.862) coincides with 6.036 in lectures, problem sets, and exams. In addition, 6.862 includes a semester-long class project and office hour like meetings to discuss the project. This website here is mainly concerned with the project part.
Instructor: Stefanie Jegelka
TAs: Jonathan Huggins, Vikas Garg
6.862 consists of all of 6.036 (lectures, problem sets, exams) and a semester-long class project (one per student). The project counts 50%.
if you are registered (or want to register) for 6.862, please fill this survey by Wed Feb 8, noon EST.
Due to resource limitations, 6.862 is restricted to graduate students and non-EECS students. To participate in the class, you should already have an idea for a project, i.e., a question in your research where machine learning could help. The class has a limited number of seats, which will be assigned based on project ideas and a lottery. Unfortunately, this means we cannot guarantee participation in class. The lottery will be announced by the end of Fri, Feb 10.
If you are an undergraduate student or EECS graduate student, you may still take 6.036 or 6.867.
Time: mandatory office hours (project feedback) by sign-up, and few general meetings to be announced. Plus 6.036 lectures, recitations. (6.036 meets Tue/Thu 1-2.30pm in 26-100.)
Location: TBA
Since 6.862 does not have independent lectures, it is not possible to audit the class. For lectures, please refer to 6.036.