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Approved UMass Amherst Courses Outside of Computer Science

The following courses have been pre-approved to count toward the MS, MS/PhD, and PhD elective requirements, subject to any overall restrictions.


BIOSTAT 690B- Intro to Causal Inference

BIOSTAT 690JQ- Biostatistics Methods 3: Modern Applied

BIOSTAT 690NR- Biostatistics Methods 2: Applied Linear

BIOSTAT 690T- Applied Statistical Genetics  

BIOSTAT 730- Applied Bayesian Statistical Modeling

BIOSTAT 740- Mixed Models and Analysis of Longitudinal Data  

BIOSTAT 748- Applied Survival Analysis  

BIOSTAT 749- Statistical Methods for Clinical Trials  

BIOSTAT 790A- Causal Inference: special topics

CIVIL & Environmental Engineering

CEE 790STA- Advanced Probabalistic Machine Learning ***


DACSS 601- Data Science Fundamentals

DACSS 602- Research Design

DACSS 603- Introduction to Quantitative Analysis

DACSS 695SL- Social Life of Algorithms

DACSS 756- Machine Learning for Social Sciences

DACSS 758- Text as Data

Courses not approved: DACSS 695C - Seminar Corporate Lobbying and the Global Economy; DACSS 690M - Math for Applied Data Science


ECON 701- Microeconomic Theory

Electrical and Computer Engineering

ECE 547- Security Engineering

ECE 556- Introduction to Cryptography

ECE 558- Intro VLSI Design

ECE 565- Digital Signal Processing

ECE 568- Computer Architecture

ECE 590C- Quantum Computing for Communication Networks

ECE 579- Math Tools for Data Science

ECE 597LS- Hardware Design for Machine Learning Systems

ECE 603- Probability and Random Processes

ECE 606- Electromagnetic Field Theory

ECE 608- Signal Theory

ECE 634- Optimal Control of Dynamic Systems

ECE 647- Security Engineering

ECE 656- Introduction to Cryptography

ECE 671- Computer Networks

ECE 674- Green Computing

ECE 697A- Advanced Computer Networks and Wireless Systems

ECE 697BE- Introduction to Biosensors and Bioelectronics

ECE 697CS- Introduction to Compressive Sensing

ECE 697LP- Design Principles for Low Power Embedded Computer Systems

ECE 697LS- Hardware Design for Machine Learning

ECE 697SN- Online Social Networks

ECE 735- Stochastic Control Dynamic Systems

ECE 745- Advanced Communication Theory

ECE 746- Statistical Signal Processing

Information Security

INFOSEC 690F- Fraud Detection

INFOSEC 690R- Information Risk Management 

INFOSEC 690S- System Defense and Test **


LINGUIST 509- Introduction to Computational Linguistics*

LINGUIST 510- Introduction to Semantics

LINGUIST 603- Generative Phonology

LINGUIST 606- Phonological Theory

LINGUIST 610- Semantics and Generative Grammar

LINGUIST 692B- Formal Foundations of Linguistic Theory

LINGUIST 692C- Cognitive Modeling

*LINGUIST 509 changed to 409 as of 9/1/2024 and is no longer applicable to the MS or PhD

Mathematics & Statistics

MATH 513- Combinatorics*

MATH 523H- Introduction to Modern Analysis

MATH 532- Topics in Ordinary Differential Equations

MATH 535- Statistical Computing

MATH 545- Linear Algebra for Applied Mathematics

MATH 551- Scientific Computing

MATH 557- Linear Optimization and Polytopes

MATH 590STA- Introduction to Mathematical Machine Learning

MATH 605- Probability Theory

MATH 611- Algebra I

MATH 612- Algebra II

MATH 623- Real Analysis I

MATH 624- Real Analysis II

MATH 651- Numerical Analysis I

MATH 652- Numerical Solution of PDE's

Math 655- Biomed and Health Data Analysis

MATH 671- Topology

MATH 697CM: ST-Combinatorial Optimization

MATH 697FA: ST-Math Foundations/Probabalistic AI 2

MATH 697PA: ST-Math Foundations/Probabalistic AI

MATH 697U- Stochastic Processes and Applications

MATH 706 Stochastic Calculus

STAT 501- Methods of Applied Statistics

STAT 511- Multivariate Statistical Methods

STAT 525- Regression Analysis

STAT 526- Design of Experiments

STAT 535- Statisical Computing

STAT 597BD/Math 655- Biomed and Health Data Analysis

STAT 607- Mathematical Statistics I

STAT 608- Mathematical Statistics II

STAT 610- Bayesian Statistics

STAT 625- Regression Modeling

STAT 697ML- Statistical Machine Learning

STAT 697MV- Applied Multivariate Statistics

STAT 697TS- Time Series Analysis and Applications

STAT 697U Stochastic Processes and Applications  

STAT 708- Applied Stochastic Models and Methods

Courses not approved: STAT 506, 515 & 516

*MATH 513 is cross-listed with COMPSCI 575

Mechanical and Industrial Engineering

MIE 532- Network Optimization

MIE 620- Linear Programming

MIE 670- Technical Project Management

MIE 684- Stochastic Processes in Industrial Engineering I

MIE 697U- Strategy-driven Engineering Innovation

MIE 724- Nonlinear & Dynamic Programming

Courses not approved: MIE 671- Product Mangement 


Physics 564- Advanced Quantum Mechanics

Physics 605- Methods Math Physics

Physics 614- Quantum Mechanics I

Physics 615- Quantum Mechanics II

School of Management

SCH-MGMT 597 FA Foundations of Accounting

SCH-MGMT 602 Business Intelligence and Analytics

SCH-MGMT 644 Economic Analysis for Managers

SCH-MGMT 650 Statistics for Business

SCH-MGMT 657 Data Science for Business

SCH-MGMT 680 Leadership and Organizational Behavior

SCH-MGMT 697DM  Web Analytics for Digital Marketing

SCH-MGMT 697RT Artificial Intelligence for Business

Courses not approved: SCH-MGMT 597FF, 797FF, 697CV, 660, 609, 821 & 822

**This class is a computer science class as of Fall 2020. Therefore, if the class was taken as CS 590A Systems Defense and Test it is counted as a computer science class. 

***This class may not be used toward the MS degree if COMPSCI 688 and COMPSCI 689 are also being counted toward the MS degree. It may be used if only one of the aforementioned courses is used.

Note: Any course not on this pre-approved list not count towards the MS degree. Exceptions may be granted, provided they are requested well before the end of the add/drop period and explicitly approved by the MS program director. Exception requests are reviewed on a case-by-case basis and are not guaranteed to be approved. In order to request an exception, please do the following:

  • Send an email to the master's program director at mpd@cs.umass.edu and cc: Eileen Hamel, associate director of graduate programs (hamel@umass.edu). 
  • The subject line of the email should read "Outside Course Approval for..."
  • The body of the email should include a detailed syllabus and weekly schedule of the course as well as a description of the number and nature (e.g., programming or otherwise) of assignments and projects.