Artificial Intelligence: Computational Structures for AI Systems Major
Artificial Intelligence (AI) is the study of creating computer systems that learn from data and interactions to create systems that can reason, generate text and images, and interact in ways that resemble humans. The Bachelor of Science in Artificial Intelligence: Computational Structures for AI Systems (BSAI) is a highly technical program that trains students to build new AI systems and algorithms from the ground up, understand the training of models from both a data and systems perspective, and then use those techniques in interdisciplinary applications. The first two years of the program develop a strong technical foundation in programming and understanding the theory of statistical learning, symbolic reasoning, and optimization. The following two years focus on deploying those techniques in interdisciplinary applications enabling students to build state-of-the-art AI systems and also think critically about the effective, ethical application of AI.
The BSAI will offer specializations in:
- Generative AI;
- AI Algorithms;
- Accessibility; and
- AI, Society, and Decision Making
Program Learning Outcomes
- Understand and apply core principles of modern and classic AI.
- Design and build efficient, scalable and effective algorithms and systems for real-world applications.
- Configure, train and deploy existing models for real-world applications.
- Critically assess AI tools and outputs to ensure reliability, efficiency, safety and alignment with intended goals.
- Apply insights from ethics and social sciences to interdisciplinary applications of AI to address societal challenges and risks of AI such as bias, transparency, fairness and accountability.
- Collaborate and communicate effectively in multidisciplinary teams to design AI solutions that address diverse perspectives and priorities.
Requirements:
| Course | Title | Credits |
|---|---|---|
| Core Requirements | ||
| CSAI101 | (Introductory Seminar) | 1 |
| MATH140 | Calculus I | 4 |
| or MATH340 | Multivariable Calculus, Linear Algebra and Differential Equations I (Honors) | |
| Introduction to Programming (one of the following): | 3-4 | |
CMSC141 | (Programming with Purpose I: Data-Centric Computing) | |
| Object-Oriented Programming I | ||
| Object-Oriented Programming for Information Science | ||
| MATH141 | Calculus II | 4 |
| Preferences and Rankings (one of the following): | 3 | |
CSAI220 | (Measuring Preferences and Rankings) | |
| Applied Probability and Statistics I | ||
| PHIL211 | AI & ETHICS | 3 |
| INST204 | Designing Fair Systems | 3 |
| Foundations of Artificial Intelligence Algorithms (one of the following): | 3-4 | |
CSAI221 | (Classical AI Algorithms) | |
| Introduction to Artificial Intelligence | ||
| Continuation of Programming (one of the following): | 4 | |
| Object-Oriented Programming II | ||
CMSC142 | (Programming with Purpose II: Data Structures and Algorithms) | |
| Sampler (one of the following): | 3 | |
CSAI102 | (Introduction to AI and the Law) | |
CSAI103 | (Introduction to AI and Food) | |
CSAI104 | (Introduction to AI and Creativity) | |
| CMSC250 | Discrete Structures | 4 |
| or DATA250 | Discrete Mathematics | |
| Linear Algebra (one of the following): | 3-4 | |
| Introduction to Differential Equations and Linear Algebra for Engineers | ||
| Introduction to Linear Algebra | ||
| Introduction to Linear Algebra and Differential Equations | ||
| Multivariable Calculus, Linear Algebra, Differential Equations II (Honors) | ||
| Linear Algebra for Scientists and Engineers | ||
| CMSC351 | Algorithms | 3 |
| Classification/Data Science (one of the following): | 3 | |
| Data Science Techniques | ||
| Introduction to Data Science | ||
| Introduction to Data Science | ||
| Introduction to Data Science and Machine Learning | ||
| CSAI216 | (Efficient Systems for AI Applications) | 4 |
| Specialization (choose one from below) | 18 | |
| Total Credits | 66-69 | |
Specializations:
General Specialization
| Course | Title | Credits |
|---|---|---|
| Required Course: | ||
| CSAI473 | (Capstone in Artificial Intelligence) | 3 |
| Electives (take five of the following): | 15 | |
| Introduction to Machine Learning | ||
| Game Programming | ||
| Computer Vision | ||
| Computer Graphics | ||
| Algorithms for Data Science | ||
| Introduction to Natural Language Processing | ||
| Introduction to Computational Game Theory | ||
| Robotics Perception and Planning | ||
| Selected Topics in Computer Science (CMSC498E Robotics) | ||
| Selected Topics in Computer Science (CMSC498Y Statistical Inference and Machine Learning Methods for Genomics Data) | ||
CSAI427 | (Reinforcement Learning) | |
CSAI461 | (Multiagent Systems) | |
| Special Topics in Immersive Media (IMDM498E Creative Experiments with AI) | ||
| Emerging Technologies and Risk Management | ||
INST436 | (User Modeling and Personalization) | |
| Human and Animal Intelligence | ||
| Total Credits | 18 | |
Generative AI Specialization
| Course | Title | Credits |
|---|---|---|
| Required Courses: | ||
| CSAI370 | (Multilingual Text Processing and Evaluation) | 3 |
| LING200 | Introductory Linguistics | 3 |
| LING240 | Language and Mind | 3 |
| CSAI424 | (Multimodal Generation) | 3 |
| Electives (take two of the following): | 6 | |
| Computer Vision | ||
| Computer Graphics | ||
| Introduction to Natural Language Processing | ||
| Introduction to Computational Game Theory | ||
CSAI432 | (AI and Human Creativity) | |
CSAI473 | (Capstone in Artificial Intelligence) | |
| Syntax I | ||
| Phonetics | ||
| Phonology I | ||
| Phonology II | ||
| Grammar and Meaning | ||
| Child Language Acquisition | ||
| Total Credits | 18 | |
AI, Society, and Decision Making Specialization
| Course | Title | Credits |
|---|---|---|
| Required Courses: | ||
| CMSC401 | Algorithms for Geospatial Computing | 3 |
| CSAI460 | (AI and the Life of Great Cities) | 3 |
| GEOG398 | Special Topics in Geography (GEOG398E Introduction to Spatial Artificial Intelligence) | 3 |
| INST366 | Privacy, Security and Ethics for Big Data | 3 |
| Electives (take two of the following): | 6 | |
| Introduction to Computational Game Theory | ||
CSAI433 | (Trust, Design, and AI) | |
CSAI435 | (Are Robots Taking our Jobs?) | |
CSAI473 | (Capstone in Artificial Intelligence) | |
CSAI491 | (AI Clinic) | |
| Public Leaders and Active Citizens | ||
| Innovation and Social Change: Creating Change for Good | ||
| Ethical, Policy and Social Implications of Science and Technology | ||
| Introduction to Sociology | ||
| Social Aspects of Artificial Intelligence | ||
| Social Dimensions of Privacy and Surveillance | ||
| Smart Machines and Human Prospects | ||
| Digital Technology and Society | ||
| Gender, Race and Computing | ||
| Total Credits | 18 | |
AI Algorithms Specialization
| Course | Title | Credits |
|---|---|---|
| Required Courses: | ||
| CMSC422 | Introduction to Machine Learning | 3 |
| CMSC472 | Introduction to Deep Learning | 3 |
| CSAI427 | (Reinforcement Learning) | 3 |
| CSAI461 | (Multiagent Systems) | 3 |
| Electives (take two of the following): | ||
| Algorithms for Geospatial Computing | ||
| Computer Vision | ||
| Algorithms for Data Science | ||
| Introduction to Natural Language Processing | ||
| Introduction to Computational Game Theory | ||
| Robotics Perception and Planning | ||
CSAI424 | (Multimodal Generation) | |
| Total Credits | 12 | |
Accessibility Specialization
| Course | Title | Credits |
|---|---|---|
| Required Courses: | ||
| CMSC434 | Introduction to Human-Computer Interaction | 3 |
| INST401 | Design and Human Disability and Aging | 3 |
| WGSS105 | Introduction to Disability Studies | 3 |
| Electives (take three of the following): | 9 | |
| (Dis)ability in American Film | ||
| Introduction to Data Visualization | ||
| Programming Handheld Systems | ||
| Introduction to Natural Language Processing | ||
CSAI431 | (AI and UX) | |
| User-Centered Design | ||
| Designing Patient-Centered Technologies | ||
| Total Credits | 18 | |
Click here for roadmaps for graduation plans in the College of Computer, Mathematical, and Natural Sciences.
Additional information on developing a graduation plan can be found on the following pages:
- http://4yearplans.umd.edu
- the Student Academic Success-Degree Completion Policy section of this catalog