Undergraduate Minor in Machine Learning
Machine learning and statistical methods are increasingly used in many application areas including natural language processing, speech, vision, robotics, and computational biology. The Minor in Machine Learning allows undergraduates to learn about the core principles of machine learning.
Students should apply for admission at least one semester before their expected graduation date, but are encouraged to apply as soon as they have taken the prerequisite classes for the minor. Grades from the core courses, 10-401/-601 and 36-401, are also welcomed with the application. An admission decision will usually be made within one month.
Requirements for the Minor
- All courses for the ML Minor, including pre-requisites, must be passed with at least a C.
- The core courses (10-401/10-601 and 36-401) must average to at least a 3.0 (i.e., 1 A and 1 C, or 2 Bs).
- The student's overall, university-wide QPA must remain at least 2.5.
- CS background: 15-122 Principles of Imperative Computation
- One year calculus: 21-120 and 21-122 or equivalent
- One year of probability & statistics: 36-217, 36-225 or 21-325, followed by 36-226 or 36-326
Double Counting Restrictions:
No course in the Machine Learning minor, other than the pre-requisites, may be counted towards another SCS minor. Additionally, at least 33 units must be used for only the Machine Learning minor, not for any other major or minor.
Core Courses - 21 units:
- 10-401 or 10-601 Machine Learning
- 36-401 Modern Regression (This course is often oversubscribed, especially for non-statistics majors. ML minors should be aware of this and register immediately when slots are available.)
Electives - Total of 36 units (e.g., three 12-unit courses) from the options below:
- A year-long senior research project in machine learning
- An approved graduate-level machine learning course
- An advanced data analysis courses from statistics
- A combination of two related courses, where one provides an introduction to a field that uses machine learning methods, and the second is in the same discipline and includes a significant machine-learning component
These options are explained in detail on the ML Minor Electives page.
The ML Director of Undergraduate Studies is William Cohen (firstname.lastname@example.org). Please contact him about eligibility, curriculum, etc. He will also help match students with faculty for projects.
How To Apply
Send email to Professor William Cohen, including the following:
- Your full name
- Andrew ID
- Preferred email address (if different)
- The semester you intend to graduate
- Your overall QPA, and your grades in any core courses (10-401/-601 and 36-401) you have taken.
- All (currently) declared majors and minors, or your home college if no major declared.
- A one page statement of purpose describes why you want to take this minor, and how it relates to your career goals.
- A proposed schedule of the courses you plan to take to satisfy the minor. (The plan is not a commitment and can be revised later.)