Navigating the Georgia Tech Machine Learning Masters Curriculum: A Comprehensive Guide
Georgia Tech offers a variety of pathways for students seeking advanced knowledge and skills in machine learning. From specialized tracks within the Online Master of Science in Computer Science (OMSCS) to interdisciplinary programs like the MS Analytics, prospective students have numerous options to tailor their education to their specific interests and career goals. This article provides a comprehensive overview of the machine learning curricula available at Georgia Tech, drawing upon detailed course information to guide students in making informed decisions.
Online Master of Science in Computer Science (OMSCS) with Machine Learning Specialization
Georgia Tech's OMSCS program is a popular and affordable option for students seeking a master's degree in computer science with a specialization in machine learning. The program is known for its flexibility, allowing students to study at their own pace and from anywhere in the world.
Curriculum Structure
The OMSCS program requires a total of 30 credit hours. Within the Machine Learning specialization, the curriculum is divided into core courses and electives:
Core Courses (6 hours): Students must select one course from each of the following two categories:
Algorithms: This category focuses on the theoretical foundations of algorithms. Students can choose from a range of courses, including:
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- CS 6505 Computability, Algorithms, and Complexity
- CS 6515 Introduction to Graduate Algorithms
- CS 6520 Computational Complexity Theory
- CS 6550 Design and Analysis of Algorithms
- CS 7510 Graph Algorithms
- CS 7520 Approximation Algorithms
- CS 7530 Randomized Algorithms
- CSE 6140 Computational Science and Engineering Algorithms
Machine Learning: This category provides a foundational understanding of machine learning principles and techniques. Students can choose from:
- CS 7641 Machine Learning
- CSE 6740 Computational Data Analysis: Learning, Mining, and Computation
Electives (9 hours): Students must choose three elective courses related to machine learning. These courses allow students to delve deeper into specific areas of interest. Elective ML courses must have at least 1/3 of their graded content based on Machine Learning. Some examples include:
- CS 6220 Big Data Systems & Analysis
- CS 6476 Computer Vision
- CS 6603 AI, Ethics, and Society
- CS 7280 Network Science
- CS 7535 Markov Chain Monte Carlo
- CS 7540 Spectral Algorithms
- CS 7545 Machine Learning Theory
- CS 7616 Pattern Recognition
- CS 7626 Behavioral Imaging
- CS 7642 Reinforcement Learning and Decision Making
- CS 7643 Deep Learning
- CS 7644 Machine Learning for Robotics
- CS 7646 Machine Learning for Trading
- CS 7650 Natural Language
- CSE 6240 Web Search and Text Mining
- CSE 6242 Data and Visual Analytics
- CSE 6250 Big Data for Health
- ISYE 6416 Computational Statistics
- ISYE 6420 Bayesian Methods
- ISYE 6664 Stochastic Optimization
Free Electives (15 hours): Students can choose any courses offered through the OMSCS program to fulfill these requirements.
Foundational Coursework Requirement
To continue in the OMSCS program after the first 12 months, students must complete two foundational courses with a grade of B or better. A list of foundational courses is available on the OMSCS website, denoted with an asterisk (*).
MS Analytics
The MS Analytics (MSA) program is an interdisciplinary curriculum focused on data science and analytics. It is designed to be completed in a single year and requires a total of 36 credits. The program emphasizes a blend of machine learning, visualization, data pipelining, statistical and operations research modeling, and application.
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Curriculum Structure
- Interdisciplinary Core (15 credits): This core covers computing, statistics, operations research, and business.
- Electives (15 credits): Students choose electives to specialize in a specific track, such as:
- Analytical Tools: Focuses on machine learning, statistical, and operations research models.
- Computational Data Analytics: Emphasizes data science, including ML, deep learning, natural language processing, AI, visualization, databases, and high-performance computing. Electives also cover data acquisition and data engineering.
- Applied Practicum: Students work with a company or organization on a real-world data science or analytics project.
Learning-How-to-Learn Component
The MSA program emphasizes the ability to adapt to new technologies and techniques. Students are trained to quickly learn new software, languages, and platforms as they emerge.
Artificial Intelligence Specialization
The Artificial Intelligence specialization is designed for students with a background in algorithms and computational thinking.
Curriculum
- Core Courses (9 hours):
- One course from:
- CS 6300 Software Development Process
- CS 6301 Advanced Topics in Software Engineering
- CS 6505 Computability, Algorithms, and Complexity
- CS 6515 Introduction to Graduate Algorithms
- CSE 6140 Computational Science and Engineering Algorithms
- Two courses from:
- CS 6601 Artificial Intelligence
- CS 7637 Knowledge-Based AI
- CS 7641 Machine Learning
- One course from:
- Electives (6 hours):
- Pick two courses from:
- Interaction
- CS 6440 Introduction to Health Informatics
- CS 6460 Educational Technology: Conceptual Foundations
- CS 6465 Computational Journalism
- CS 6471 Computational Social Science
- CS 6603 AI, Ethics, and Society
- CS 6750 Human-Computer Interaction
- AI Methods
- CS 6476 Computer Vision
- CS 7631 Multi-Robot Systems
- CS 7632 Game AI
- CS 7633 Human-Robot Interaction
- CS 7634 AI Storytelling in Virtual Worlds
- CS 7643 Deep Learning
- CS 7647 Machine Learning with Limited Supervision
- CS 7650 Natural Language
- Cognition
- CS 6795 Introduction to Cognitive Science
- CS 7610 Modeling and Design
- CS 7651 Human and Machine Learning
- Interaction
- Pick two courses from:
Computational Perception and Robotics Specialization
This specialization focuses on the intersection of perception, robotics, and artificial intelligence.
Curriculum
- Core Courses (6 hours):
- One course from:
- CS 6505 Computability, Algorithms, and Complexity
- CS 6515 Introduction to Graduate Algorithms
- CS 6520 Computational Complexity Theory
- CS 6550 Design and Analysis of Algorithms
- CS 7520 Approximation Algorithms
- CS 7530 Randomized Algorithms
- CSE 6140 Computational Science and Engineering Algorithms
- One course from:
- CS 6601 Artificial Intelligence
- CS 7641 Machine Learning
- One course from:
- Electives (9 hours):
- Pick three courses from Perception and Robotics, with at least one course from each.
- Perception
- CS 6475 Computational Photography
- CS 6476 Computer Vision
- CS 7499 3D Reconstruction
- CS 7636 Computational Perception
- CS 7639 Cyber Physical Design and Analysis
- CS 7644 Machine Learning for Robotics
- CS 7650 Natural Language
- Robotics
- CS 7630 Autonomous Robotics
- CS 7631 Autonomous Multi-Robot Systems
- CS 7633 Human-Robot Interaction
- CS 7638 Artificial Intelligence Techniques for Robotics
- CS 7648 Interactive Robot Learning
- CS 7649 Robot Intelligence: Planning
- Perception
- Pick three courses from Perception and Robotics, with at least one course from each.
Computer Graphics Specialization
This specialization explores the principles and techniques behind computer graphics.
Curriculum
- Core Courses (6 hours):
- One course from:*CS 6491 Foundations of Computer Graphics*CS 6457 Video Game Design*CS 7496 Computer Animation
- One course from:
- CS 6505 Computability, Algorithms, and Complexity
- CS 6515 Introduction to Graduate Algorithms
- Electives (9 hours):
- Pick three from:*CS 6457 Video Game Design and Programming*CS 6475 Computational Photography*CS 6476 Computer Vision*CS 6491 Foundations of Computer Graphics*CS 6492 Shape Grammars*CS 6730 Data Visualization Principles*CS 7450 Information Visualization*CS 7496 Computer Animation
Computing Systems Specialization
The Computing Systems specialization focuses on the design and implementation of computer systems.
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Curriculum
- Core courses (9 hours):
- CS 6505 Computability, Algorithms, and Complexity or CS 6515 Introduction to Graduate Algorithms
- Pick two of:
- CS 6210 Advanced Operating Systems
- CS 6241 Compiler Design
- CS 6250 Computer Networks
- CS 6290 High-Performance Computer Architecture
- CS 6300 Software Development Process OR CS 6301 Advanced Topics in Software Engineering
- CS 6390 Programming Languages
- CS 6400 Database Systems Concepts and Designs
- Electives (9 hours):
- Pick three courses from:
- CS 6035 Introduction to Information Security
- CS 6200 Graduate Introduction to Operating Systems
- CS 6220 Big Data Systems and Analytics
- CS 6235 Real Time Systems
- CS 6238 Secure Computer Systems
- CS 6260 Applied Cryptography
- CS 6262 Network Security
- CS 6263 Intro to Cyber Physical Systems Security
- CS 6291 Embedded Software Optimization
- CS 6310 Software Architecture and Design
- CS 6340 Software Analysis and Testing
- CS 6365 Introduction to Enterprise Computing
- CS 6422 Database System Implementation
- CS 6423 Advanced Database System Implementation
- CS 6550 Design and Analysis of Algorithms
- CS 6675 Advanced Internet Computing Systems and Applications
- CS 7210 Distributed Computing
- CS 7260 Internetworking Architectures and Protocols
- CS 7270 Networked Applications and Services
- CS 7280 Network Science
- CS 7290 Advanced Topics in Microarchitecture
- CS 7292 Reliability and Security in Computer Architecture
- CS 7560 Theory of Cryptography
- CSE 6220 High Performance Computing
- Pick three courses from:
External Machine Learning Programs
Several other universities offer online master's programs with a focus on machine learning. These programs vary in their technical depth, target audience, and cost. Some notable examples include:
- Columbia University: Online MSCS with a Machine Learning specialization.
- Stevens Institute of Technology: Online MS in Machine Learning.
- Drexel University: Online MSAIML.
- George Washington University (GW): Online MEng in AI & ML.
- University of Wisconsin (UW): Online MS in AIML for Engineering.
- University of Illinois Chicago (UIC): Online MEng â AI & ML.
- Rice University: Online Master of Data Science (MDS) â Machine Learning.
- Worcester Polytechnic Institute (WPI): Online Master of Science in Data Science (MSDS) â AI & ML.
- Duke University: Online MEM â Data Analytics & Machine Learning.
- New York University (NYU): Online MS in Emerging Technologies â ML & AI.
- Purdue University: Online Master of Science in Artificial Intelligence (MSAI).
- Boston University: Online Master of Science (MS) in Applied Data Analytics (ADA) â AI & Machine Learning.
- University of New Mexico (UNM): Online MSCE â Applied ML & AI.
PhD in Machine Learning
Georgia Tech also offers a PhD program in Machine Learning for students interested in pursuing original, independent research.
Curriculum
- Core Curriculum (12 hours): Four core courses to provide a broad foundation in machine learning.
- Area Electives (15 hours): Five elective courses to gain depth in specific areas of ML.
- Responsible Conduct of Research (RCR) (1 hour): Online component and in-person training.
- Qualifying Examination (3 hours):
- Doctoral Minor (6 hours): Two courses outside the area of Machine Learning.
Machine Learning Series
Georgia Tech offers a Machine Learning Series, initially created by Charles Isbell and Michael Littman, designed for graduate students and working professionals.
Course Overview
The course progresses from supervised learning to unsupervised learning and reinforcement learning. It requires familiarity with Python, linear algebra, probability, and basic calculus.
Course Content
The course covers a range of topics, including:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
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