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:
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Establish a career as a machine learning scientist in either industry or academia.
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Develop a deep understanding of machine learning theory and become familiar with the most important machine learning paradigms.
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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:
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Understand the theory underlying machine learning algorithms.
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Use machine learning to make decisions and predictions.
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Select appropriate statistical and predictive methodologies.
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Build statistical learning models and understand their strengths and limitations.
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Provide appropriate interpretation of classification or regression results.
Degree Requirements
The program is a 30-credit degree program. Students are required to complete:
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4 machine learning core courses (12 credits)
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3 machine learning core electives (9 credits)
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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 541 | Artificial Intelligence | 3 |
| CS 559 | Machine Learning: Fundamentals and Applications | 3 |
| CS 560 | Statistical Machine Learning | 3 |
| CS 583 | Deep Learning | 3 |
| CS 584 | Natural Language Processing | 3 |
| CS 589 | Text Mining and Information Retrieval | 3 |
Machine Learning Core Electives Requirements: Complete Three of the Courses Mentioned Below
| CS 513 | Knowledge Discovery and Data Mining | 3 |
| CS 532 | 3D Computer Vision | 3 |
| CS 544 | Health Informatics | 3 |
| CS 556 | Mathematical Foundations of Machine Learning | 3 |
| CS 558 | Computer Vision | 3 |
| CS 582 | Causal Inference | 3 |
| CS 589 | Text Mining and Information Retrieval | 3 |
| CS 598 | Visual Information Retrieval | 3 |
| CS 609 | Data Management and Exploration on the Web | 3 |
| BIA 654 | Experimental Design II | 3 |
| BIA 660 | Web Mining | 3 |
| BIA 662 | Augmented Intelligence and Generative AI | 3 |
| BIA 678 | Big Data Technologies | 3 |
| CPE 608 | Applied Modeling and Optimization | 3 |
| CPE 595 | Applied Machine Learning | 3 |
| MA 541 | Statistical Methods | 3 |
| MA 630 | Advanced Optimization Methods | 3 |
| MA 641 | Time Series Analysis I | 3 |
| MA 661 | Dynamic Programming and Reinforcement Learning | 3 |
| CS 800 | Special Problems in Computer Science (M.S.) | 1 - 6 |
| CS 900 | Thesis in Computer Science (M.S.) | 1 - 10 |
General Electives
Up to three general electives, which can be any graduate course.