6.036 Introduction to Machine Learning (Spring 2016)

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.

Teaching staff (6036-staff@lists.csail.mit.edu): (please read this page carefully before contacting the staff)

Lectures: (tentative schedule)


Exams (60% of grade):

  1. Midterm on March 17, closed book, 1.5h, in-class, 20% of grade. If you have a conflict with the exam time, please notify us of your midterm conflict at least two weeks before the exam. The conflict exam (with permission only) will be the next day (Friday, March 18). In exceptional cases, the weight of the midterm can be shited to the final exam.

  2. Final exam during finals week (time tbd), closed book, 3h, 40% of grade. Any conflicts with the final exam time are handled by the registrar's office.

Assignments (40% of grade in total, schedule):

  1. 5 regular homework assignments (10% of the grade in total).

  2. Three projects, each worth 10% of the grade, involving programming in python/MATLAB.