Students are encouraged to enroll in the spring of their sophomore year, but no later than the start of their senior year.

For enrollment in the minor program, it is required to have your major declared beforehand.

Please use this form to enroll: Minor Enrollment Application

For questions, contact us at [email protected].

Please Note:

These are not hard prerequisites for the minor, but may be required for some of the courses in the minor and are thus recommended for students who intend to apply. We recommend that students plan what courses they intend to take in the minor and aim to fulfill the prerequisites of those courses.

Coding

(Normally completed by the end of the sophomore year)

COS 126/POL 345/SML 201

COS 126 provides comprehensive coverage of coding principles. A student can also learn coding in R⁽¹⁾ within the narrower contexts of statistics (POL 345) or data science (SML 201). It is recommended that all students continue to hone their coding by learning Python⁽¹⁾ through co-curricular coding courses.

Mathematics

(Normally completed before the spring semester of the junior year)

Linear Algebra: MAT 202/EGR 154/SML 305⁽²⁾⁽³⁾

Calculus: MAT 201 or (MAT 103, then SML 305)

Probability

Taking one of the approved core statistics courses provides a basic understanding of probability. An additional course from ORF 309/SML 305 is strongly recommended for students interested in advanced machine learning courses.

Notes:

⁽¹⁾ Python and R are the most frequent coding languages in statistics and machine learning.

⁽²⁾ SML 305 covers the key aspects of linear algebra, differential calculus, and probability, most relevant to statistics and machine learning courses.

⁽³⁾ SML 305 will not count towards the minor program.

Students must take five courses from approved lists and earn a grade of B- or better in each course (pdf or advanced placement are not allowed). With permission, advanced students can take approved graduate-level courses.

Required Course Work:

• One statistics course from an approved list.
• One machine learning course from an approved list.
• Three additional courses from the approved list of elective courses or (with approval) additional non-cognate courses from the statistics and machine learning approved lists.

Students may count a maximum of two courses from their major toward the minor.

Independent Work:

Students are required to complete at least one semester of independent work in their junior or senior year on a topic that applies SML methods or investigates these methods. This work may be used to satisfy the IW requirement of the SML minor and the student's major. All work will be reviewed by the Statistics and Machine Learning Minor committee. In May, there will be an (online) poster session in which students must present their independent work to other students, researchers, and the faculty. Students must adhere to submission due dates for independent work papers and poster requirements.

Students are encouraged to attend the CSML-sponsored or co-sponsored colloquia and seminars

Examples of possible paths through the minor.

Students must take five courses from approved lists and earn a grade of B- or better in each course (pdf or advanced placement are not allowed). With permission, advanced students can take approved graduate-level courses.

Foundations of Statistics (Must take one)

• ECO 202 Statistics & Data Analysis for Economics⁽¹⁾
• ORF 245 Fundamentals of Statistics⁽¹⁾
• POL 345/SOC 305 Intro to Quantitative Social Science⁽¹⁾
• PSY 251 Quantitative Methods⁽¹⁾
• SPIA 200 Statistics for Social Science⁽¹⁾

⁽¹⁾ The following statistics courses: ECO 202, ORF 245, POL 345/SOC 305, PSY 251, SPI 200, differ in style, application domains, and some advanced topics, but have sufficient overlap to be considered cognates.

Foundations of Machine Learning (Must take one)

• COS 324 Introduction to Machine Learning⁽²⁾
• COS 424/SML 302 Fundamentals of Machine Learning
• ECE 364 Machine Learning for Predictive Data Analytics⁽²⁾
• ECE 435 Machine Learning and Pattern Recognition
• MAT 490 Mathematical Introduction to Machine Learning
• ORF 350 Analysis of Big Data⁽²⁾

⁽²⁾ COS 324, ECE 364 and ORF 350 are all introductory ML courses. The courses differ in focus and details, but are sufficiently similar in intent and content to be considered cognates. In addition, these courses are not recommended for students who have already taken a 4xx AI/ML course.

Electives (Must take three)

Data Science

• POL 245 Visualizing Data
• SML 201 Introduction to Data Science
• SML 301 Data Intelligence: Modern Data Science Methods⁽³⁾⁽⁴⁾
• SML 310/312 Research Projects in Data Science⁽³⁾⁽⁴⁾

⁽³⁾ Students who take both SML 301 and SML 310/312 will only receive credit for one course.

⁽⁴⁾ SML 310 and SML 312 are project based courses, while SML 301 is a lecture based course. However, there is considerable overlap in course content. Hence a student can take at most one of these courses.

Machine Learning

• COS 429 Computer Vision
• COS 484 Natural Language Processing
• COS 485 Neural Networks –Theory and Applications
• ORF 418 Optimal Learning
• SOC/SML 306 Machine Learning with Social Data: Opportunities and Challenges

Theory

• ECE/COS 434: Machine Learning Theory
• MAT 385 Probability Theory
• ORF 309 Probability and Stochastic Systems
• ORF 363 Computing and Optimization for the Physical and Social Sciences

Applications

• AST 303 Modeling and Observing the Universe: Research Methods in Astrophysics
• CEE 460 Risk Analysis
• ECO 302 Econometrics
• ECO 312 Econometrics: A Mathematical Approach
• ECO 313 Econometric Applications
• ECO 491: Financial Risk Management
• ECE 382 Probabilistic Systems and Information Processing
• ECE 480/NEU 480/PSY 480 fMRI Decoding: Reading Minds Using Brain Scans
• GEO 422 Data, Models, and Uncertainty in the Natural Sciences
• MAE 345 Robotics and Intelligent Systems
• NEU 330/PSY 330: Computational Models of Psychological Functions
• ORF 405 Regression and Applied Time Series
• ORF 473 FinTech: Data Driven Innovation
• POL 346 Applied Quantitative Analysis
• PSY 360/COS 360: Computational Models of Cognition
• PSY 454/COS 454 Probabilistic Models of Cognition
• QCB 408 Foundations of Applied Statistics and Data Science
• QCB 455/551 Introduction to Genomics and Computational Molecular Biology
• SOC 400 Applied Social Statistics

Examples of possible paths through the minor.

Summer Research Award

The Center for Statistics and Machine Learning's Undergraduate Remote Summer Research Award will provide a grant(s) to currently enrolled Princeton UG student(s) to help cover the expenses of working remotely on a summer research project with a faculty advisor/mentor. Applications must meet the requirements listed below and include all attachments and explanations requested. The max amount per award is $3,200. If you have questions, please email [email protected]

All applications and payments will be made through SAFE.

Requirements:

• Princeton enrolled UG students (preference given to CSML program students)
• Research over the summer with a Princeton faculty advisor/mentor ~ include a letter of nomination and support from the faculty mentor.
• Research must be relevant to statistics and machine learning - include an outline of the research plan.
• $3,200 max award (meant to cover 8 weeks of remote summer research)
• Short write up about how this research project will impact your education here at Princeton and beyond (no more than one page)

Apply Here

Scholarly Travel Fund

The Center for Statistics and Machine Learning's Undergraduate Scholarly Travel Fund will provide a grant(s) to currently enrolled Undergraduate program student(s) to present at a conference, seminar or workshop in a field closely related to statistics and machine learning. Applications must meet the requirements listed below and include all attachments and explanations requested. The max amount of an award for this academic year is $500 and may be distributed to one or more students. Awards will be reviewed and granted by CSML on a rolling basis. If you have questions, please email [email protected]

Requirements:

• SML Undergraduate program enrolled students only
• Travel/event within current academic year-must be before graduation if senior
• Must present at conference (talk or poster)-include a description of your work
• Conference must be relevant to statistics and machine learning - include a summary of the event you plan to attend, including an explanation of the relevance
• Submit a copy of the acceptance letter for your work being presented
• CSML support should be acknowledged in the talk or poster at the event
• $500 max award

Apply Here

Study Abroad Policy

Students in the SML undergraduate minor can utilize up to two non-Princeton/study abroad courses for the SML minor, with proper approval.

An equivalent course needs to be offered at Princeton University and that course needs to be on the SML  approved course list. It is the student’s responsibility to obtain approval and sign off from the faculty member teaching that course. SML cannot sign off on a course that is not our course. Our courses are SML 201, 310, 312, etc.

Once you receive approval and the signature from the appropriate faculty member, please forward the signed form to [email protected] for the CSML program director to sign off.

Please plan and leave enough time to obtain these signatures and approvals.
 
Example: You would like to take a course at another University that is comparable to ORF 245 - Fundamentals of Statistics. The faculty member teaching ORF 245 must sign off since they are the one that can verify the course is similar to the one, they teach. Once that faculty signs off, email [email protected] the signed forms for CSML program director’s signature.
 
Example: You would like to take a course at another University and there is not an equivalent course offered at Princeton University. SML cannot approve the course and it cannot be used for the SML minor.

Can I include data analysis in my independent work for the SML minor program if I'm a history major?
No. Unfortunately, you have to use statistics and/or machine learning in your independent work. However, the breadth of these methods have expanded beyond the sciences. For example, the digital humanities makes heavy use of data analysis methods.

To what extent do I need to use statistics and/or machine learning? Do I need to develop my own study/methodology?
Statistics and/or machine learning must play a major role in your independent work. You do not necessarily have to develop new methods but you must apply statistical and/or machine learning methods to your question of interest. The independent work is a culmination of your education at Princeton: you need to demonstrate that you can apply methods you've learned in order to answer research questions. We will also have a poster session where you will be presenting your work to SML faculty.

Can I fulfill the requirement with a more advanced course than those listed in the "Fundamentals of Statistics" or "Fundamentals of Machine Learning" category?
Yes, that is possible but students must obtain permission of the program director. In addition, students are required to take a total of 5 courses in order to be qualified for a minor in SML.

If I can only take 2 courses from one major, is the example path for CS students incorrect?
So long as students are not using more than two courses towards another degree program (i.e., departmental major), they are allowed to count these courses towards the SML minor.

Is PDF acceptable for courses?
No, you must take 5 courses and earn at least a grade of B- for each course.

Can I present at the poster session during my junior year?
Yes, if you have fulfilled the independent work requirement and submitted the work to the faculty committee on time.

Can I do a joint presentation at the poster session with another student pursuing the SML minor?
No, your independent work or thesis must be done and presented independently to receive credit.

Can I submit an independent work during my senior year that is not my senior thesis?
Yes, you can submit an independent work separate from your senior thesis.

How can I propose a different course for the minor (e.g., if I am studying abroad)?
You must submit a PDF copy of the official course syllabus to the Academic Program Coordinator and the Program Director. The syllabus must include information about the course materials and assignments. Please see our policy on study abroad courses.

Can my AP Statistics exam score count for credit or exempt me from the statistics requirement?
No, AP exams do not provide credit or exemptions for the SML minor program.

Can I count multiple courses from the Foundations of Statistics course list towards the SML minor?
No, multiple courses in the Fundamentals of Statistics category will not count towards the minor program.

Can I use my JP or senior thesis from another department for my SML IW?
Yes, you can.  As long as the project has a substantial machine learning or statistics component to it, you may submit work that was done for another program. Please confirm with the other program or department.

Can I use a collaborative project with a graduate student in a lab for my SML IW?
Yes, as long as your own work within the project has a substantial machine learning or statistics component.

Can I use my work in SML 310/312 as part of my undergraduate thesis?
With permission from their thesis advisor and/or their undergraduate department, students can incorporate work they did in SML 310/312 into their thesis.

Can I use my work in SML 310/312 to fulfill the SML Independent Work requirement?
That is in principle possible if the scope of your SML 310 project is sufficiently large and the write-up is sufficiently comprehensive. However, many SML 310 students would need to expand their SML 310/312 project in order to fulfill the IW requirement.

Can I use work from an earlier project (e.g., my JP) as my project in SML 310/312?
You cannot resubmit work that you already have completed elsewhere. You can build on work that you had done previously. Your write-up must clearly indicate what part of the work was done as part of SML 310/312, and what part of the work was done earlier.