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Master of Science in Machine Learning

The Master of Science in Machine Learning degree aims to provide theoretical and practical foundations that enable its graduates to be at the forefront of progress in machine learning and related disciplines. Machine learning is a rapidly expanding field with many applications in diverse areas, such as intelligent systems, perception, robotics, finance, information retrieval, bioinformatics, healthcare, weather prediction among others. In addition to established employers in these industries, a large number of startups have also entered the market seeking to hire machine learning scientists. Besides careers in industry, this program will prepare students for the pursuit of doctoral degrees and careers in research.

Program Objectives

 

The program prepares students to:

  • Establish a career as a machine learning scientist in either industry or academia.

  • Develop a deep understanding of machine learning theory and become familiar with the most important machine learning paradigms.

  • Select and apply appropriate machine learning models on real-world applications in various areas of science and engineering.

Program Outcomes

Upon completion of the 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 program is a 30-credit degree program. Students are required to complete:

  • 4 machine learning core courses (12 credits)

  • 3 machine learning core electives  (9 credits) 

  • 3 general electives (9 credits), which can be any graduate course

Students must maintain a minimum grade of a C or above in any course and a minimum GPA of 3.000.

Machine Learning Core Requirements: Complete Four out of the Five Courses Mentioned Below:

CS 541Artificial Intelligence

3
CS 559Machine Learning: Fundamentals and Applications

3
CS 560Statistical Machine Learning

3
CS 583Deep Learning

3
CS 584Natural Language Processing

3

Machine Learning Core Electives Requirements: Complete Three of the Courses Mentioned Below

CS 513Knowledge Discovery and Data Mining

3
CS 5323D Computer Vision

3
CS 544Health Informatics

3
CS 556Mathematical Foundations of Machine Learning

3
CS 558Computer Vision

3
CS 582Causal Inference

3
CS 589Text Mining and Information Retrieval

3
CS 598Visual Information Retrieval

3
CS 609Data Management and Exploration on the Web

3
BIA 654Experimental Design II

3
BIA 660Web Mining

3
BIA 662Augmented Intelligence and Generative AI

3
BIA 678Big Data Technologies

3
CPE 608Applied Modeling and Optimization

3
CPE 695Applied Machine Learning

3
FE 541Applied Statistics with Applications in Finance

3
MA 541Statistical Methods

3
MA 630Advanced Optimization Methods

3
MA 641Time Series Analysis I

3
MA 661Dynamic Programming and Reinforcement Learning

3
CS 800Special Problems in Computer Science (M.S.)

1-6
CS 900Thesis in Computer Science (M.S.)

1-10

General Electives

Up to three general electives, which can be any graduate course.