In an increasingly data-driven world, the MS in Statistics & Machine Learning prepares you for a range of career possibilities in statistics and machine learning and helps you stand out in the marketplace.

The need for professionals trained in the mathematical techniques of machine learning is greater than ever. In this program, you’ll work closely with faculty who have extensive backgrounds in applied mathematics, data science/analytics, computational science, statistics, industrial modeling, and much more. In lieu of one formal course, students may conduct a semester’s worth of independent study with a research advisor that leads to publication of a quality technical report in an area of statistical/data sciences.

Admission to the program requires a BA/BS in math or statistics; if you have an undergraduate degree in a different discipline, you may be admitted if you have adequate undergraduate training in mathematics (multivariate calculus, linear algebra), computing (including familiarity with one or more programming languages, e.g., R, MATLAB, Python), and probability/statistics.

This program is STEM designated, allowing international students who hold F-1 visas to apply for OPT work authorizations for a total of 36 months (an initial 1-year period and a 24-month OPT STEM extension) of paid work experience in the U.S. after graduation.

Program at a Glance

32 units

2 years

*This estimate assumes full-time registration and pursuit of the degree. Actual completion times will vary and may be higher, depending on full- or part-time course registration, units transferred, and time to complete other degree requirements.

Fall | Spring

MS in Statistics & Machine Learning

Featured Courses

MATH 351
Time Series Analysis

Undertakes an analysis of time series data by means of such particular models as ARIMA, spectral analysis, and associated methods of inference and applications.

MATH 352
Nonparametric & Computational Statistics

Analyzes the treatment of statistical questions that do not depend on specific parametric models.

MATH 353
Asymptotic Methods in Statistics With Applications

Examines modes of convergence for random variables and their distributions, central limit theorems, laws of large numbers, statistical large sample theory of functions of sample moments, and more.

MATH 355
Linear Statistical Models

Discusses linear statistical models in full and less-than-full rank cases, the Gauss-Markov theorem, and applications to regression analysis, analysis of variance, and analysis of covariance.

MATH 256
Stochastic Processes

Analyzes properties of independent and dependent random variables and examines conditional expectation among topics chosen from Markov processes and others.

Math 462
Mathematics of Machine Learning

Covers theoretical foundations, algorithms, and methodologies for machine learning, emphasizing the role of probability and optimization and exploring a variety of real-world applications.

View All Mathematics Courses


Degree Requirements
A minimum of eight courses (32 units) of graduate math coursework is required, including four core courses (16 units) and two restricted electives (8 units). Students who lack the prerequisite undergraduate coursework may be asked to complete more than 32 units. At least 20 units of coursework must be gamma courses (300-level and above). A grade of B- or above must be earned in gamma courses.

Students in the master’s program in mathematics, computational and applied mathematics, and statistical sciences may convert one 200-level (beta) course to gamma credit. At the discretion of the IMS Director, in consultation with the student’s academic advisor, more than one conversion may be approved in exceptional cases.

Core Courses: 4 courses (16 units)
At least one core course must be selected from each category.

  • Statistics
    • Math 352 Nonparametric and Computational Statistics
    • Math 353 Asymptotic Methods in Statistics with Applications
    • Math 355 Linear Statistical Models
  • Machine Learning
    • Math 454 Statistical Learning
    • Math 462 Mathematics of Machine Learning
    • Math 364 Machine Learning for Asset Pricing
  • Applied Statistics
    • Math 359 Computational Statistics
    • Math 466 Advanced Big Data Analysis
    • Math 452 Large-scale Inference
    • Math 366 Data Mining

Restricted Electives: 2 electives (8 units)
Two restricted electives can be chosen from either the list of core courses, or the following list (not all these courses are offered every year)

  • Math 251 Probability (prerequisite for Math 252)
  • Math 252 Statistical Theory (prerequisite for all 300+ level statistics courses)
  • Math 256 Stochastic Processes
  • Math 293-393 Mathematics Clinic
  • Math 306 Optimization
  • Math 351 Time Series Data Analysis
  • Math 365 Statistical Methods in Molecular Biology
  • Math 389 Advanced Topics in Mathematics (if appropriate, with advisor’s approval)

Unrestricted Electives: 2 electives (8 units)

  • Any CGU Math course
  • CGH 301 Biostatistics
  • Any relevant course from other programs (see below)

Independent Study
In lieu of one formal course, students may take Math 398 Independent Study with a research advisor leading to a publication quality technical report in an area of statistical/data sciences.

Subject to approval by their academic advisor, students working outside campus on mathematical/statistical projects may also use this professional experience as the basis of a Math 398 Independent Study. At most 2 units per semester can be acquired in this practical type of independent study.

Additional course option
Subject to approval by their academic advisors, students may choose as unrestricted electives one or two graduate courses from within other departments at CGU or at KGI in which statistics and/or machine learning are extensively applied. The fields may include Economics, Finance, Community and Global Health, Information Science and Technology, Evaluation, Education, Psychology, etc.

Accelerated Degree Option

If you are an undergraduate student at the Claremont Colleges (Pomona, Scripps, Claremont McKenna, Harvey Mudd, Pitzer), you can obtain a graduate degree on an accelerated track through the Claremont Graduate Scholars Program, working toward your master’s requirements simultaneously with the completion of your undergraduate degree. Up to 16 units of transferable credit can be earned upon admission to one of our master’s degree programs. You are eligible for a minimum fellowship award of $5,000 per semester at CGU, based on 12 units of enrollment. Learn how to apply

Consider this opportunity if:

  • Your longer-term plans include a PhD in a math-related field (e.g. physics, engineering, computer science)
  • You are seeking a teaching career in math or science
  • You would like to get involved in ongoing research with CGU Math faculty during your senior year
  • You would like to earn your Master’s degree in half the usual time


Engineering & Computational Mathematics Clinic
CGU’s internationally recognized Engineering & Computational Mathematics Clinic offers first-hand experience solving significant mathematical problems for industry and government clients.

Recent projects include:

  • Optimizing Transmission of Renewable Energy–Southern California Edison
  • Hardware-Software Codesign–Los Alamos National Laboratory
  • Data Cohort Analysis–Fair Isaac
  • Optimizing Smart Power Grids–Los Alamos National Laboratory
  • Credit Risk in a Network Economy–Fitch Rating
  • Isogeometric Analysis–Boeing
  • Gate to Base Capacitance Modeling for Nanoscale MOSFETs–USC Information Sciences Institute
  • Practical Semi-Analytic Model for the Substrate Current of Short Channel MOSFETs with LDDs–USC Information Sciences Institute

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Claremont Center for the Mathematical Sciences
Through the Claremont Center for the Mathematical Sciences (CCMS), you’ll have access to one of the largest mathematical science communities in California, as well as to workshops, conferences, and seminars, including:

  • Southern California Analysis Seminar
  • Math-in-Industry Workshop
  • Michael E. Moody Lecture Series
  • History and Philosophy of Mathematics Seminar
  • Claremont Mathematics Weekend
  • CCMS Software Lab
  • and more

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Faculty & Research
  • John Angus profile image

    John Angus

    Professor of Mathematics

    Research Interests

    Probability, Statistics, Computing, Algorithms, Navigation, Systems Engineering, Mathematical Finance

  • Marina Chugunova profile image

    Marina Chugunova

    Professor of Mathematics
    Director, Institute of Mathematical Sciences
    Program Director, PhD in Engineering & Computational Mathematics

    Research Interests

    Surfactant-driven thin film flows in biomedical applications; Nonlinear parabolic equations; Stability problems in fluid dynamics; Scientific computations; Applied operator theory; Sturm-Liouville problems

  • Hrushikesh Mhaskar profile image

    Hrushikesh Mhaskar

    Research Professor of Mathematics

    Research Interests

    Approximation theory, Computational harmonic analysis, Machine learning, Signal processing

  • Ali Nadim profile image

    Ali Nadim

    Professor of Mathematics
    Joseph H. Pengilly Chair in Mathematics

    Research Interests

    Fluid Dynamics, Mathematical Modeling, Scientific Computing

  • Andrew Nguyen profile image

    Andrew Nguyen

    Adjunct Professor of Mathematics

    Research Interests

    Stochastic processes, Statistics, Risk management, Financial derivatives, Actuarial sciences, Statistical software

  • Qidi Peng profile image

    Qidi Peng

    Research Associate Professor of Mathematics

    Research Interests

    Statistical inferences, Stochastic differential equations, Stochastic modeling, Simulation, Machine learning, Approximation theory, Graph theory

  • Allon Percus profile image

    Allon Percus

    Professor of Mathematics

    Research Interests

    Discrete optimization; Network models; Statistical physics; Random combinatorial structures

  • Claudia Rangel-Escareño profile image

    Claudia Rangel-Escareño

    Adjunct Professor of Mathematics

    Research Interests

    Probabilistic methods in computational biology, Statistical inference of genetic networks, Bioinformatics

  • Henry Schellhorn profile image

    Henry Schellhorn

    Professor of Mathematics
    Academic Director, Financial Engineering Program

    Research Interests

    Financial engineering, Credit risk, Stochastic analysis, Traffic models

Extended Faculty

In addition to CGU core faculty, you will have access to Math faculty across the Claremont Colleges, including Pomona, Scripps, Harvey Mudd, Claremont McKenna, Pitzer, and Keck Graduate Institute, as well as faculty who are part of the W.M. Keck integrated science department.

View Full Faculty List

Harvey Mudd
View Math Faculty

View Math Faculty

Claremont McKenna
View Math Faculty

View Math Faculty

David Bachman
Jim Hoste

W.M. Keck Integrated Science Department
(Claremont McKenna, Pitzer, Scripps)
Adam Landsberg
John Milton

Keck Graduate Institute
Animesh Ray
James Sterling

Where You Can Find Our Alumni

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