Introduction to Machine Learning
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Below are review or practice materials available for the midterm exam, from previous semesters of 6.390 (6.036).
These materials may be updated between now and the exam, as we add/release more information.
We encourage you to work the problems on your own first, and then later check your answers with the solutions or explanations.
Recent Example Exams
Older Example Problems and Exams
Note: some of the exams below covered topics that have now been removed from the course. You are not responsible for these topics, including perceptrons (and margin maximization), and support vector machines (and hinge loss).
- Midterm Fall 2019: Exam, Solutions
- Midterm Spring 2019: Exam, Solutions
- Midterm Fall 2018: Exam, Solutions
- Midterm Spring 2018: Exam, Solutions, Explanations
- Midterm Review - Fall 2018: Problems, Solutions, Explanations
Review Problems by Topic
A selection of past exam problems, grouped by topic: Problems, Solutions
- ML process
- Hypothesis class
- Linear regression
- Logistic regression
- Gradient descent
- Nearest Neighbors
- Neural Networks