Machine Learning Master's Degree Curriculum Overview

The machine learning master’s degree program at Stevens consists of five core courses including Artificial Intelligence, deep learning and natural language processing, and an interdisciplinary list of electives. Courses focus on both theoretical analysis and implementation of a wide range of topics in machine learning. Students can optionally work on a thesis with one of the program's faculty members.

By the end of this program, students will be able to:

  • Understand the theory underlying machine learning algorithms
  • Use machine learning to make decisions and predictions
  • Select appropriate statistical and predictive methodologies
  • Build statistical learning models and understand their strengths and limitations
  • Provide appropriate interpretation of classification or regression results

Degree Requirements: 

The Master of Engineering in Machine Learning requires 30 graduate credits in an approved plan of study, including at least four core courses and three electives. Participation in research, via CS 800 or a master's thesis via CS 900 are optional. You may take up to three graduate courses as free electives.

Core Courses Include:

  • CS 559 Machine Learning: Fundamental and Applications
  • CS 541 Artificial Intelligence
  • CS 583 Deep Learning
  • CS 584 Natural Language Processing
  • CS 560 Statistical Machine Learning

Electives Include:

  • CS 513 Knowledge Discovery and Data Mining
  • CS 532 3D Computer Vision
  • CS 545 Human Computer Interaction
  • CS 544 Health Informatics
  • CS 558 Computer Vision
  • CS 582 Causal Inference
  • CS 598 Visual Information Retrieval
  • CS 609 Data Management and Exploration on the Web
  • BIA 654 Experimental Design
  • BIA 660 Web Analytics
  • BIA 678 Big Data Technologies
  • CPE 608 Applied Modeling and Optimization
  • CPE 695 Applied Machine Learning
  • FE 541 Applied Statistics with Applications in Finance
  • MA 541 Statistical Methods
  • MA 630 Advanced Optimization Methods
  • MA 641 Time Series Analysis
  • MA 661 Dynamic Programming and Learning
  • CS 800 Special Problems in Computer Science (with one of the program faculty members, up to 6 credits)
  • CS 900 MS Thesis in Computer Science (with one of the program faculty members, 5-10 credits)

If you have existing graduate credits or experience in this area of study, contact [email protected] to discuss opportunities to include it in the curriculum.