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
- CS background: 15-122 Principles of Imperative Computation (Must pass with a C for this class)
- 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, no more than 24 units for the ML minor can be double counted toward any other major or minor.
- 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 three courses (36 credits) from the options below:
- A year-long senior project, supervised or co-supervised by a ML Faculty member. (Normally this will be conducted as two semester-long projects.) Samples of ML Minor Senior Projects
- An approved 700 or 800-level course from the Machine Learning Department, such as 10-701 Machine Learning; 10-702 Statistical Machine Learning; 36-705 Intermediate Statistics; 15-826 Multimedia Databases and Data Mining; 15-853 Algorithms in the Real World; 10-605 Machine Learning with large Datasets; 15-859 (B) Machine Learning Theory; 10-802 Analysis of Social Media.
- 36-700 Probability Theory and Statistics I. This course covers material similar to 36-705, but at a level more appropriate to master's students and advanced undergraduates. (Note that 36-700 is not open to students that have taken 36-326 Honors Statistics.) Note: 36-700 and 10-705 cover similar material, and it is not recommended that students take both courses.
- Any advanced data analysis courses from statistics, for example 36-402 Advanced Data Analysis; 36-315 Statistical Graphics and Visualization; or courses from the 36-46x series (such as 36-461 Statistical Methods for Epidemiology, 36-462 Chaos, Complexity and Inference, 36-463 Hierarchical Models, and 36-464 Multivariate Methods.)
- A combination of two related courses, from the minor electives page, 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.
Students should note that several of these courses are primarily aimed at graduate students, and should make sure that they are adequately prepared for them before enrolling.
The ML Director of Undergraduate Studies is William W. Cohen (email@example.com). Please contact him about eligibility, curriculum, etc. He will also help match students with faculty for projects.
How To Apply
Send email to William Cohen (firstname.lastname@example.org), include 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. Minors are expected to maintain a QPA of 3.0 in core courses and 2.5 overall.
- 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.)