Artificial Intelligence, MS (Boston)

The Master of Science in Artificial Intelligence provides a comprehensive framework encompassing foundational algorithms, theory, and practical applications in the rapidly evolving field of AI. This program offers students the essential knowledge and skills to design, develop, and implement AI systems across various high-demand sectors. The core curriculum is designed to provide in-depth understanding of fundamental AI concepts, forming a solid foundation for both theoretical and practical aspects of artificial intelligence.

Building on this strong foundation, students can delve into advanced AI applications through interdisciplinary concentrations tailored to industry needs. These concentrations include computer vision, continuous process engineering, machine learning, and robotics and agent-based systems.

The master's in artificial intelligence is an interdisciplinary degree offered by Khoury College of Computer Sciences, the College of Engineering, Bouvé College of Health Sciences, and the College of Arts, Media and Design. Descriptions of the concentrations offered in this degree are as follows:

• Bioengineering—College of Engineering

  • Students in the bioengineering concentration seek to develop the knowledge and skills to integrate emerging machine learning approaches with engineering principles to understand and interact with complex biological systems. Coursework focuses on using quantitative and data-driven techniques in the context of medical diagnostics, biological signal processing, drug discovery, personalized medicine, and genomic medicine. Successful graduates are prepared to develop AI-driven engineering approaches toward designing, optimizing, and improving healthcare solutions, pharmaceuticals, and innovative medical intervention techniques.

• Computational Creativity—College of Arts, Media and Design

  • This interdisciplinary concentration is designed to empower students to explore the frontiers where artificial intelligence meets human creativity and artistic expression. Students engage with cutting-edge AI techniques to generate, analyze, and augment creative works across visual arts, music, interactive media, and design. 

    Students have an opportunity to synthesize technical AI foundations with specialized creative computing expertise, developing the versatility to innovate across diverse creative industries—from data visualization and digital art to games and extended reality. Whether developing AI systems that collaborate with human artists or creating autonomous creative agents that push artistic boundaries, students tackle authentic challenges reshaping the creative landscape. Throughout the concentration, students grapple with essential questions about authorship, authenticity, and the evolving relationship between human creativity and machine intelligence in creative practice.  

• Computer Vision—College of Engineering

  • The computer vision concentration offers students an opportunity to obtain the knowledge and skills to drive innovation at the intersection of new and emerging vision systems and AI. Through utilizing AI and machine learning solutions for a wide range of applications, the concentration covers image enhancement/restoration, object recognition, navigation, graphics rendering, and pattern classification.

• Continuous Process Engineering—College of Engineering

  • ​The continuous process engineering concentration offers students an opportunity to obtain the knowledge and skills to revolutionize how industrial processes are designed, monitored, and optimized. The program aims to prepare students to utilize AI and machine learning solutions to enhance process efficiency, predictive maintenance, and real-time decision making in industries such as chemicals, pharmaceuticals, and energy. Successful graduates are prepared to integrate AI-driven solutions into continuous manufacturing environments to improve safety, sustainability, and productivity.

• Energy Systems—College of Engineering

  • The energy systems concentration offers students an opportunity to obtain the knowledge and skills to drive innovation in sustainable and intelligent energy systems. Students examine how to utilize AI and machine learning solutions to support smart energy grid operations, integrate renewable energy sources, and create responsive and resilient systems.

• Health Data—Bouvé College of Health Sciences

  • This interdisciplinary concentration is designed to prepare students to tackle pressing challenges at the intersection of artificial intelligence and health. Students work directly with real-world health and wellness data, applying cutting-edge AI techniques to problems that matter. Students integrate technical skills from their core coursework with specialized health data expertise, gaining versatility to work across the entire health ecosystem, from clinical settings to public health to digital health startups. Whether building prototype AI-driven health interventions or uncovering insights from complex datasets, students engage with authentic challenges facing the field today. Throughout the concentration, students address critical questions about equitable health technology design and responsible AI deployment in high-stakes healthcare contexts.

• Human-AI Collaboration Systems—College of Engineering
  • The goal of the human-AI collaboration concentration is to prepare engineers to design and deploy AI systems that enhance human performance in manufacturing  and sociotechnical systems. Rooted in the College of Engineering’s mission to drive engineering-driven problems, the program emphasizes applied machine learning, human-centered design, and responsible AI.

Coursework focuses on using machine learning, AI, and human factors engineering and systems design to develop AI solutions that improve productivity, decision making, and safety in human-in-the-loop environments. Students gain hands-on experience integrating AI into real-world settings such as smart manufacturing, autonomous operations, sociotechnical systems, human-machine interactions, engineering management, and are trained to lead interdisciplinary teams advancing intelligent, collaborative systems.  Graduates will be well-suited for roles such as AI systems engineers for high-tech companies and smart  manufacturing companies, human-centered AI designer, and technical lead for cross-functional teams in automation and decision-support applications.

• Machine Learning—Khoury College of Computer Sciences

  • The goal of the machine learning concentration is to provide students with practical, hands-on experience in designing, implementing, and optimizing machine learning models for real-world applications. The curriculum positions graduates to pursue roles that leverage AI to drive innovation and efficiency in a wide variety of industries and domains.

• Omics Concentration—College of Science

  • The omics concentration will provide students with foundational and advanced understanding of genomics, transcriptomics, proteomics, and related omics domains, while emphasizing applications of machine learning and AI algorithms to biological data analysis.

• Robotics and Agent-Based Systems—Khoury College of Computer Sciences

  • The goal of the robotics and agent-based systems concentration is to provide students with expertise in designing and developing intelligent robotic systems and autonomous agents capable of performing complex tasks in dynamic environments. The curriculum positions graduates to pursue roles that contribute to innovations in automation, human-robot interaction, and intelligent systems design.

• ​Sustainability for Infrastructure and Environment—College of Engineering

  • Offered through the Department of Civil and Environmental Engineering, this concentration is designed to prepare students with the knowledge and skills to create impactful next-generation solutions at the intersection of the natural and built environments. Students are prepared to utilize AI and machine learning solutions to create smarter, healthier cities; to ensure continued and optimized functions of critical infrastructure in the face of a changing climate; and to support closed-loop material management across all sectors.

Students are admitted to the college associated with their concentration, and their degree is awarded by that college. Students follow all policies associated with their college of admission. 

Beginning with students admitted in the fall of 2026, all students pursuing on-ground master's degree programs must complete a university-level experiential learning requirement. This requirement may be fulfilled through the curriculum, co-op, or other work- or industry-integrated learning experiences.

Complete all courses and requirements listed below unless otherwise indicated.

Core Requirements

A cumulative GPA of 3.000 or higher is required in the following:

Course List

Code	Title	Hours
CS 5100	Foundations of Artificial Intelligence	4
CS 5130 and CS 5131	Applied Programming and Data Processing for AI and Recitation for CS 5130	4
DADS 5200	Mathematics for Machine Learning	4
or DS 5020	Introduction to Linear Algebra and Probability for Data Science
EECE 5644	Introduction to Machine Learning and Pattern Recognition	4
or DADS 7275	Machine Learning and Data Analytics

Concentrations

Complete one of the following:

• Bioengineering—College of Engineering
• Computational Creativity—College of Arts, Media and Design
• Computer Vision—College of Engineering
• Continuous Process Engineering—College of Engineering
• Energy Systems—College of Engineering
• Health Data—Bouvé College of Health Sciences
• Human-AI Collaboration Systems—College of Engineering
• Machine Learning—Khoury College of Computer Sciences
• Omics—College of Science
• Robotics and Agent-Based Systems—Khoury College of Computer Sciences
• Sustainability for Infrastructure and Environment—College of Engineering

Program Credit/GPA Requirements

32 total semester hours required  (additional semester hours required for participation in optional co-op in certain concentrations)
Minimum 3.000 GPA required

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Bioengineering Concentration—College of Engineering 

Course List

Code	Title	Hours
Complete 8 semester hours from the following:		8
BIOE 5740	Intermediate Computing Skills for Bioengineers
BIOE 5750	Modeling and Inference in Bioengineering
BIOE 5880	Computational Methods in Systems Bioengineering
Complete 4 semester hours from the following:		4
BIOE 5115	Dynamical Systems in Biological Engineering
BIOE 5860	Engineering Approaches to Precision Medicine I
PHIL 5010	AI Ethics
PHTH 5350 and PHTH 6130	Using SAS in Public Health Research and Public Health Technologies: Ethics and Equity
Capstone
BIOE 5770	Machine Learning Methods in Biology and Health	4
Optional Co-op
Complete the following (students must complete ENCP 6100 to qualify for co-op experience):
ENCP 6100	Introduction to Cooperative Education	1
ENCP 6964	Co-op Work Experience	0
or ENCP 6954	Co-op Work Experience - Half-Time
or ENCP 6955	Co-op Work Experience Abroad - Half-Time
or ENCP 6965	Co-op Work Experience Abroad

Computational Creativity Concentration—College of Arts, Media and Design

Course List

Code	Title	Hours
Required Courses
CRTE 6500	Creative Technologies Studio	4
INAM 5000	Introduction to Creative Computing	4
Option 1		4
Select one elective from the following:
ARTG 5000	Topics in Design
ARTG 5310	Visual Cognition
ARTG 5330	Visualization Technologies 1: Fundamentals
ARTG 6460	Human-Centered AI
GSND 6460	Generative Game Design
INAM 6430	AI and Creative Exploration
JRNL 6460	AI in Media Industries
PHIL 5010	AI Ethics
Capstone
CRTE 7500	Creative Technologies Project	4
Optional Co-op
Complete the following:
EEAM 6964	Co-op Work Experience	0
or EEAM 6954	Co-op Work Experience - Half-Time
or EEAM 6955	Co-op Work Experience Abroad - Half-Time
or EEAM 6965	Co-op Work Experience Abroad

Computer Vision Concentration—College of Engineering 

Continuous Process Engineering Concentration—College of Engineering

Course List

Code	Title	Hours
Complete 8 semester hours from the following (students without a chemical engineering degree are advised to choose CHEM 5101 and CHEM 5102, which serve as a bridge from other degrees):		8
CHME 5101 and CHME 5102	Fundamentals of Chemical Engineering: Fluid, Heat, and Mass Transfer and Fundamentals of Chemical Engineering: Thermodynamics and Kinetics
CHME 5510 and CHME 5515	Fundamentals in Process Safety Engineering and Process Safety Engineering for Biotechnology and Pharmaceutical Industries
CHME 7600 and CHME 7601	Pharmaceutical Engineering I and Pharmaceutical Engineering II
Complete 4 semester hours from the following:		4
CHME 5137	Computational Modeling in Chemical Engineering
CHME 5520	Designing for Process Safety
CHEM 5641 and CHME 7901	Computational Chemistry and Journal Club in Chemical Engineering
CHME 5649	Numerical Strategies and Data Analytics for Chemical Sciences
IE 7270	Intelligent Manufacturing
PHIL 5010	AI Ethics
PHSC 5900	AI in Drug Discovery and Development
Capstone
CHME 6580	Artificial Intelligence for Process Engineering Capstone	4
Optional Co-op
Complete the following (students must complete ENCP 6100 to qualify for co-op experience):
ENCP 6100	Introduction to Cooperative Education	1
ENCP 6964	Co-op Work Experience	0
or ENCP 6954	Co-op Work Experience - Half-Time
or ENCP 6955	Co-op Work Experience Abroad - Half-Time
or ENCP 6965	Co-op Work Experience Abroad

Energy Systems Concentration—College of Engineering

Course List

Code	Title	Hours
Complete 12 semester hours from the following:		12
CHME 5621	Electrochemical Engineering
CHME 5692	Carbon Capture, Utilization, and Storage
ENSY 5000	Fundamentals of Energy System Integration
ENSY 5700	Renewable Energy Development
ENSY 5800	Applications of Artificial Intelligence in Energy Systems
MATL 6270	Principles, Devices, and Materials for Energy Storage and Energy Harvesting
ME 5374	Special Topics in Mechanical Engineering
ME 6200	Mathematical Methods for Mechanical Engineers 1
PHIL 5010	AI Ethics
Capstone
ME 7945	Master’s Project	4
Optional Co-op
Complete the following (students must complete ENCP 6100 to qualify for co-op experience):
ENCP 6100	Introduction to Cooperative Education	1
ENCP 6964	Co-op Work Experience	0
or ENCP 6954	Co-op Work Experience - Half-Time
or ENCP 6955	Co-op Work Experience Abroad - Half-Time
or ENCP 6965	Co-op Work Experience Abroad

Health Data Concentration—Bouvé College of Health Sciences

Course List

Code	Title	Hours
HLTH 5810	Survey of Health-Related Data	4
HLTH 5820	AI Project for Health Applications	4
Complete 4 semester hours from the following:		4
HINF 5300	Personal Health Interface Design and Development
HINF 5500	Artificial Intelligence and Health Informatics
HLTH 5550	Product Design, Development, and Innovation in Health Science
NNMD 5310	Bioethics in the Age of Artificial Intelligence
PHIL 5010	AI Ethics
PHSC 5905	Artificial Intelligence and Machine Learning in Drug Discovery: Concepts and Applications
PHTH 5320	Grant Writing in Public Health
PHTH 5350	Using SAS in Public Health Research
PHTH 5360	AI at the Intersection of Health and Society
PHTH 6130	Public Health Technologies: Ethics and Equity
Complete 4 semester hours from the following:		4
HLTH 6980	Capstone
HLTH 7995	Master's Project
Optional Co-op
Complete the following (students must complete HLTH 5101 to qualify for co-op experience):
HLTH 5101	Professional Development for Bouvé Graduate Co-op	1
HLTH 6964	Co-op Work Experience	0
or HLTH 6954	Co-op Work Experience - Half-Time
or HLTH 6955	Co-op Work Experience Abroad - Half Time
or HLTH 6965	Co-op Work Experience Abroad

Human-AI Collaboration Systems—College of Engineering

Course List

Code	Title	Hours
Complete12 semester hours from the following:		12
CSYE 7280	User Experience Design and Testing
DADS 5250	Generative AI in Practice
IE 5374	Special Topics in Industrial Engineering
IE 5400	Healthcare Systems Modeling and Analysis
IE 6300	Manufacturing Systems Design
IE 6500	Human Performance
IE 7270	Intelligent Manufacturing
IE 7295	Applied Reinforcement Learning in Engineering
IE 7350	Sociotechnical Systems: Computational Models for Design and Policy
ME 5510	Scientific Machine Learning for Mechanical Engineers
Capstone
IE 7945	Master’s Project	4
Optional Co-op
Complete the following (students must complete ENCP 6100 to qualify for co-op experience):
ENCP 6100	Introduction to Cooperative Education	1
ENCP 6964	Co-op Work Experience	0
or ENCP 6954	Co-op Work Experience - Half-Time
or ENCP 6955	Co-op Work Experience Abroad - Half-Time
or ENCP 6965	Co-op Work Experience Abroad

Machine Learning Concentration—Khoury College of Computer Sciences

Course List

Code	Title	Hours
CS 5800	Algorithms	4
Complete 8 semester hours from the following:		8
CS 5180	Reinforcement Learning and Sequential Decision Making
CS 5330	Pattern Recognition and Computer Vision
CS 6120	Natural Language Processing
CS 6200	Information Retrieval
CS 6220	Data Mining Techniques
CS 7140	Advanced Machine Learning
CS 7150	Deep Learning
CS 7180	Special Topics in Artificial Intelligence
Capstone
Complete 4 semester hours from the following:		4
CS 6170	AI Capstone
CS 8674	Master’s Project
Optional Co-op
Complete the following:
CS 6964	Co-op Work Experience	0
or CS 6954	Co-op Work Experience - Half-Time
or CS 6955	Co-op Work Experience Abroad - Half-Time
or CS 6965	Co-op Work Experience Abroad

Omics Concentration—College of Science

Course List

Code	Title	Hours
BINF 6400	Genomics in Bioinformatics	4
BINF 6420	Omics in Bioinformatics	4
BINF 6430	Transcriptomics in Bioinformatics	4
BIOL 6407	Biochemistry for Molecular Biologists	4
Optional Co-op
Complete the following (students must complete EESC 6500 to qualify for co-op experience):
EESC 6500	Pathways to Professional Success	1
EESC 6964	Co-op Work Experience	0
or EESC 6954	Co-op Work Experience - Half-Time
or EESC 6965	Co-op Work Experience Abroad

Robotics and Agent-Based Systems Concentration—Khoury College of Computer Sciences

Course List

Code	Title	Hours
CS 5800	Algorithms	4
Complete 8 semester hours from the following:		8
CS 5180	Reinforcement Learning and Sequential Decision Making
CS 5335	Robotic Science and Systems
EECE 5550	Mobile Robotics
EECE 5554	Robotics Sensing and Navigation
Capstone
Complete 4 semester hours from the following:		4
CS 6170	AI Capstone
CS 8674	Master’s Project
Optional Co-op
Complete the following:
CS 6964	Co-op Work Experience	0
or CS 6954	Co-op Work Experience - Half-Time
or CS 6955	Co-op Work Experience Abroad - Half-Time
or CS 6965	Co-op Work Experience Abroad

Sustainability for Infrastructure and Environment Concentration—College of Engineering

Course List

Code	Title	Hours
Complete 8 semester hours from the following, including at least 4 semester hours from Applied AI in Urban/Environmental Systems:		8
Applied AI in Urban/Environmental Systems
CIVE 7100	Time Series and Geospatial Data Sciences
CIVE 7150	Data-Driven Decision Support for Civil and Environmental Engineering
CIVE 7151	Urban Informatics and Processing
Advanced AI/ML Concepts
CS 6120	Natural Language Processing
EECE 5614	Reinforcement Learning and Decision Making Under Uncertainty
EECE 7397	Advanced Machine Learning
IE 7615	Deep Learning for AI
Applications to the Discipline
Complete 4 semester hours from the following:		4
CIVE 5275	Life Cycle Assessment of Materials, Products, and Infrastructure
CIVE 5280	Remote Sensing of the Environment
CIVE 5281	Coastal Dynamics and Design
CIVE 5373	Transportation Systems: Analysis and Planning
CIVE 5524	Vibration-Based Structural Health Monitoring
CIVE 7155	Dynamics and Control of Infrastructure Systems
CIVE 7341	Structural Reliability
CIVE 7380	Performance Models and Simulation of Transportation Networks
CIVE 7381	Transportation Demand Forecasting and Model Estimation
Capstone
CIVE 7945	Master’s Project	4
Optional Co-op
Complete the following (students must complete ENCP 6100 to qualify for co-op experience):
ENCP 6100	Introduction to Cooperative Education	1
ENCP 6964	Co-op Work Experience	0
or ENCP 6954	Co-op Work Experience - Half-Time
or ENCP 6955	Co-op Work Experience Abroad - Half-Time
or ENCP 6965	Co-op Work Experience Abroad

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