Program Overview
Department of Computer Science at Stevens- Location: Hoboken, NJ
- Length: 12 Months
- Tuition: $44,000
- Website: https://www.stevens.edu/program/machine-learning-masters
The Master of Science in Machine Learning at Stevens Institute of Technology provides students with advanced training in artificial intelligence, deep learning, and data-driven methods. Available both on campus and fully online, the program offers flexibility for working professionals and international students alike. With strong placement rates and connections to tech leaders like Google, Amazon, and Bloomberg, this program is designed to prepare students for fast-growing careers in AI and data science.
Duration, Credits, and Format
The program consists of approximately 30 credits and can typically be completed in two years of full-time study. Students can choose between campus-based or fully online formats.
Courses follow a structured sequence across multiple terms and include both core requirements and electives tailored to student interests and goals.
Curriculum
Key courses include Natural Language Processing, Deep Learning, Artificial Intelligence, and Machine Learning Fundamentals. Students also choose electives such as Big Data Technologies, Web Mining, or Time Series Analysis.
The curriculum emphasizes both theoretical understanding and applied learning, including opportunities for research or thesis work.
Core Coursework
CS 541 Artificial Intelligence
This course explores AI techniques focused on learning and problem-solving using statistical modeling and modern optimization methods. Students will apply these tools to real-world applications. A background in calculus, linear algebra, probability, and programming (Python or Matlab) is required.
CS 559 Machine Learning: Fundamentals and Applications
Covers foundational machine learning principles and the implementation of core algorithms. Topics include regression, classification, clustering, and neural networks. Emphasizes trade-offs between computational efficiency and prediction accuracy, preparing students to design effective ML solutions.
CS 583 Deep Learning
Introduces deep learning methods for solving problems in computer vision and natural language processing. Students gain practical experience using Python and frameworks like TensorFlow and Keras. A background in linear algebra and Python programming is required; ML knowledge is helpful but not mandatory.
CS 584 Natural Language Processing
Focuses on applying machine learning, especially deep learning, to NLP tasks. Topics include word embeddings, RNNs, CNNs, attention models, and reinforcement learning. Students need prior coursework in linear algebra and probability or CS 556.
CS 589 Text Mining and Information Retrieval
Explores core IR techniques and advanced NLP models. Students learn TF-IDF, BM25, inverted indexing, neural translation, Transformers, and generative language models. Assignments include hands-on implementation using tools like ElasticSearch and HuggingFace.
Elective Options
CS 513 Knowledge Discovery and Data Mining
Covers essential techniques in data mining and knowledge discovery, including OLAP, neural networks, rule-based systems, fuzzy logic, machine learning, and decision trees. Students apply these methods to real-world business problems through practical case studies.
CS 532 3D Computer Vision
Introduces 3D computer vision concepts such as single/multi-view geometry, structure from motion, and 3D reconstruction. Includes analysis of 3D data and applications in robotics, augmented reality, geospatial systems, and assistive technologies.
CS 544 Health Informatics
Explores health informatics history, standards, systems, and technologies like telemedicine and mobile health. Emphasizes protocols, system evaluation, and Web services within clinical and consumer health applications.
CS 556 Mathematical Foundations of Machine Learning
Provides a rigorous introduction to the mathematics behind machine learning, covering linear algebra, calculus, probability, and algorithms like regression and SVMs. Students complete hands-on Python implementations using libraries such as NumPy and SciPy.
CS 558 Computer Vision
Focuses on computer vision algorithms and image analysis. Topics include edge detection, shape analysis, photometry, depth recovery, and object classification. Requires prior knowledge of linear algebra and data structures.
CS 582 Causal Inference
Teaches how to determine causal relationships in data using methods like Bayesian networks, Granger causality, and dynamic models. Emphasizes prediction, explanation, and intervention in domains like finance, biology, and politics.
CS 589 Text Mining and Information Retrieval
Covers text mining and IR methods including vector space models, TF-IDF, BM25, neural networks, and Transformers. Students gain hands-on skills using ElasticSearch, HuggingFace, and model optimization techniques.
CS 598 Visual Information Retrieval
Studies methods for indexing and retrieving images and videos. Covers techniques for structuring visual data, with applications in web-scale search, augmented reality, location recognition, and e-commerce.
CS 609 Data Management and Exploration on the Web
Advanced course on web data systems, including web IR, XML databases, and data integration. Draws content from top research in database systems and is designed for graduate and advanced undergraduate students.
BIA 654 Experimental Design II
Focuses on designing and analyzing experiments. Covers hypothesis testing, sampling, variables, and design strategies. Students complete a final project that involves full-cycle research from design to analysis.
BIA 660 Web Mining
Teaches how to extract and analyze large-scale web data using distributed computing. Students apply real-world web mining methods in a final project involving scientific questions or web applications.
BIA 662 Augmented Intelligence and Generative AI
Explores cognitive computing and generative AI using tools like Watson and TensorFlow. Topics include NLP, knowledge representation, and cognitive application development within big data environments.
BIA 678 Big Data Technologies
Covers tools and strategies for managing big data’s volume, velocity, and variety. Builds on business intelligence concepts and includes topics in governance, architecture, and practical implementation.
CPE 608 Applied Modeling and Optimization
Focuses on optimization techniques used in engineering and business. Topics include linear and non-linear programming, stochastic modeling, genetic algorithms, and practical case studies from various domains.
CPE 595 Applied Machine Learning
Provides an overview of machine learning algorithms including decision trees, neural networks, and reinforcement learning. Emphasizes simulation and application to real-world problems through programming assignments.
MA 541 Statistical Methods
Covers foundational statistical tools including regression, hypothesis testing, ANOVA, and nonparametric methods. Students work on projects using statistical software and real-world datasets.
MA 630 Advanced Optimization Methods
Teaches advanced optimization concepts including convex duality, decomposition methods, and large-scale problem solving. Applies theory to domains like data mining and includes training in optimization software.
MA 641 Time Series Analysis I
Introduces time series techniques for data analysis and forecasting. Topics include ARMA models, spectral analysis, and practical applications in finance, economics, and process control.
MA 661 Dynamic Programming and Reinforcement Learning
Explores dynamic programming techniques and their role in learning and decision-making systems. Topics include finite and infinite-horizon models, approximate methods, and real-world applications in various sectors.
CS 800 Special Problems in Computer Science (M.S.)
A research-oriented course allowing students to investigate current computer science topics under faculty supervision. Requires a publishable-quality report and is ideal for students not pursuing a thesis.
CS 900 Thesis in Computer Science (M.S.)
Involves original, supervised research that contributes to the field. The thesis may serve as a foundation for doctoral research and requires significant independent work under faculty guidance.
More curriculum details available here:
Tuition
Tuition is charged per credit. For the 2024–2025 academic year, the cost is $1,438 per credit for online students, bringing the total tuition to approximately $43,140 for the full degree.
More tuition information here: https://online.stevens.edu/tuition-and-financial-aid/
Admissions and Requirements
Applicants must hold a bachelor’s degree in a relevant STEM field. International students are welcome and may pursue Optional Practical Training (OPT) or Curricular Practical Training (CPT).
While GRE scores are not required, they are recommended for applicants with GPAs below 3.0.
Career Outcomes and Fit
Graduates are well-prepared for roles such as machine learning engineer, data scientist, and AI research scientist. The program is ideal for professionals with strong technical foundations who are looking to upskill in AI or shift into roles focused on automation, modeling, and data intelligence.
Recognition and Rankings
Stevens’ online machine learning program ranks among the top 10 in the nation for graduate computer information technology according to U.S. News & World Report.
The program boasts a 100% employment rate within three months of graduation and an average starting salary of $121,000 for recent graduates.
This program offers a solid foundation for individuals looking to lead in a future shaped by AI and machine learning.