Program Overview
College of Computing- Location: Atlanta, GA
- Length:
- Tuition: $5,400
- Website: https://omscs.gatech.edu/specialization-machine-learning
The Online Master of Science in Computer Science (OMSCS) with a Specialization in Machine Learning at Georgia Tech is a fully online program designed for professionals aiming to advance their expertise in artificial intelligence and data science.
It offers the same academic rigor as the on-campus degree but with the flexibility of asynchronous coursework.
Program Length and Structure
Students complete 30 credit hours and may take up to six years to graduate. Most take one or two courses per semester, allowing them to balance studies with work and personal commitments.
The degree can be earned through a course-only path, with no thesis or project requirement.
Tuition
At $180 per credit hour, the total cost of the program is approximately $5,400.
This low tuition makes OMSCS one of the most affordable graduate computer science programs in the country, especially among top-ranked institutions.
More tuition information available here: https://www.bursar.gatech.edu/tuition-fees
Curriculum and Specialization Requirements
The Machine Learning specialization includes 15 credits of targeted coursework.
Students select one algorithms course and one machine learning core course such as CS 7641 Machine Learning or CSE 6740 Computational Data Analysis. They also complete three elective courses with substantial ML content, including options like Deep Learning, Natural Language Processing, and Reinforcement Learning.
Key coursework includes:
CS 6505. Computability, Algorithms, and Complexity
Covers core concepts from computability and complexity theory, focusing on algorithm design for combinatorial, algebraic, and number-theoretic problems, as well as NP-completeness.
CS 6515. Introduction to Graduate Algorithms
Introduces graduate-level algorithm design and analysis, covering dynamic programming, divide and conquer, FFT, graph and flow algorithms, RSA, and NP-completeness.
CS 6520. Computational Complexity Theory
Explores complexity classes, resource-bounded computation, reducibility, completeness, and the theoretical limits of efficient computation.
CS 6550. Design and Analysis of Algorithms
Focuses on advanced techniques to design efficient algorithms for a range of mathematical and computational problems.
CS 7510. Graph Algorithms
Studies algorithms for graph-based problems such as shortest paths, flows, matchings, coloring, and connectivity.
CS 7520. Approximation Algorithms
Examines design and analysis of approximation algorithms to solve NP-hard problems where exact solutions are computationally infeasible.
CS 7530. Randomized Algorithms
Introduces randomized algorithm design and derandomization techniques, with applications across computer science.
CSE 6140. Computational Science and Engineering Algorithms
Focuses on algorithms essential for high-performance computing and scientific computing applications.
CS 7641. Machine Learning
Covers core machine learning methods, including inductive and analytical techniques, with practical real-world applications.
CSE 6740. Computational Data Analysis: Learning, Mining, and Computation
Focuses on computational approaches for analyzing large datasets through learning and mining techniques.
CS 6220. Big Data Systems and Analytics
Covers data analytics and system design for big data, including machine learning optimizations in real-world applications.
CS 6476. Introduction to Computer Vision GR
Introduces image formation, camera geometry, stereo vision, motion tracking, and scene understanding.
CS 6603. AI, Ethics, and Society
Addresses ethical considerations in AI, including fairness, bias, and the societal impact of machine learning.
CS 7280. Network Science: Methods and Applications
Analyzes real-world networks and models dynamic processes, co-evolution, and structural metrics.
CS 7535. Markov Chain Monte Carlo Algorithms
Studies convergence analysis and application of MCMC methods across scientific disciplines.
CS 7540. Spectral Algorithms and Representations
Explores spectral methods and their application to large-scale data analysis and learning problems.
CS 7545. Theoretical Foundations of Machine Learning
Covers mathematical tools for analyzing learning algorithms, with focus on statistical and computational theory.
CS 7616. Pattern Recognition
Introduces theory and application of pattern recognition techniques on real-world data.
CS 7626. Introduction to Behavioral Imaging
Applies sensor data and ML to model human behavior, particularly for health-related use cases.
CS 7642. Reinforcement Learning and Decision Making
Covers decision-making models including MDPs, planning, and reinforcement learning techniques.
CS 7643. Deep Learning
Explores deep neural networks, structured models, and optimization methods in AI applications.
CS 7644. Machine Learning for Robotics
Applies machine learning models like regression and deep learning to robotic control tasks.
CS 7646. Machine Learning for Trading
Introduces machine learning-based financial strategies, portfolio construction, and algorithmic trading tools.
CS 7650. Natural Language
Studies statistical and symbolic approaches to language understanding, parsing, and semantics.
CS 8803. Special Topics: Probabilistic Graph Models
Explores probabilistic methods for modeling relational data using graphical models.
CSE 6240. Web Search and Text Mining
Covers text mining and search algorithms, including crawling, indexing, ranking, and classification.
CSE 6242. Data and Visual Analytics
Introduces principles and tools for visual analysis of high-dimensional and complex datasets.
CSE 6250. Big Data for Health
Focuses on scalable machine learning systems and analytics for electronic health data.
ISYE 6416. Computational Statistics
Covers statistical computing techniques including bootstrapping, EM, and MCMC for complex models.
ISYE 6420. Bayesian Methods
Introduces Bayesian inference, conjugate priors, computation techniques, and real-world applications.
ISYE 6664. Stochastic Optimization
Covers modeling and solving Markov decision processes and sequential decision-making under uncertainty.
More information about the curriculum here: https://catalog.gatech.edu/coursesaz/isye/
Free Electives and Broader Learning
The remaining 15 credits can be fulfilled with free electives from the OMSCS course catalog. This allows students to broaden their expertise in other areas like data analytics, robotics, and software engineering.
Admissions Criteria and Requirements
Applicants should hold a bachelor’s degree in computer science or a closely related field and have a minimum GPA of 3.0. International applicants must meet English proficiency requirements
Within the first year, students must complete two foundational courses with a grade of B or higher to continue in the program.
Who Should Enroll
This program is a strong fit for software engineers, data analysts, or professionals with a quantitative background who want to move into machine learning roles. The flexible format is ideal for full-time workers and career switchers seeking academic advancement without relocating or pausing their careers.
Outcomes and Recognition
Graduates are prepared to apply ML techniques in industries such as technology, finance, healthcare, and robotics. The program is widely recognized for its affordability, academic quality, and career impact. Georgia Tech’s large alumni network and industry relationships further support career growth.