Artificial Intelligence, BSE
The rapid rise of big data, machine learning, and artificial intelligence have resulted in tremendous breakthroughs that are having horizontal impact across many disciplines, in engineering, computing and beyond. The need for cutting edge AI engineers is tremendous, as are the research and innovation opportunities in this rapidly evolving field. Above all there is tremendous potential for having a positive societal impact in numerous applications domains (health, energy, transportation, robotics, computer vision, human machine interfaces, national security) in addition to networks and society.
Curriculum
Artificial Intelligence (ARIN) Major Requirements
37 course units are required.
| Code | Title | Course Units |
|---|---|---|
| Computing | ||
| CIS 1100 | Introduction to Computer Programming | 1 |
| CIS 1200 | Programming Languages and Techniques I | 1 |
| CIS 1210 | Programming Languages and Techniques II | 1 |
| CIS 2450 | Big Data Analytics | 1 |
| CIS 3200 | Introduction to Algorithms | 1 |
| Math and Natural Science | ||
| MATH 1400 | Calculus, Part I | 1 |
| MATH 1410 | Calculus, Part II | 1 |
| or MATH 1610 | Honors Calculus | |
| CIS 1600 | Mathematical Foundations of Computer Science | 1 |
| ESE 2030 | Linear Algebra with Applications to Engineering and AI | 1 |
| ESE 3010 | Engineering Probability | 1 |
| or STAT 4300 | Probability | |
| ESE 4020 | Statistics for Data Science | 1 |
| or ESE 5420 | Statistics for Data Science | |
| Natural Science elective 1 | 1 | |
| Artificial Intelligence | ||
| 12 course units, with at least one course unit from each of the following 6 categories. Note that one course cannot satisfy multiple categories, so, e.g., if you take ESE 4210 for Optimization & Control then you must still take another Project course. | 12 | |
| Introduction to AI | ||
| Artificial Intelligence | ||
or CIS 5210 | Artificial Intelligence | |
| Artificial Intelligence Lab: Data, Systems, and Decisions | ||
| Machine Learning | ||
| Applied Machine Learning | ||
or CIS 5190 | Applied Machine Learning | |
| Machine Learning | ||
| Signals & Systems | ||
| Introduction to Dynamic Systems | ||
| Signal and Information Processing | ||
| Optimization & Control | ||
| Introduction to Optimization | ||
| Control For Autonomous Robots | ||
| Vision & Language | ||
| Natural Language Processing | ||
or CIS 5300 | Natural Language Processing | |
| Computer Vision & Computational Photography | ||
or CIS 5810 | Computer Vision & Computational Photography | |
| AI Project | ||
| Software Design/Engineering | ||
| Natural Language Processing | ||
or CIS 5300 | Natural Language Processing | |
| Computer Vision & Computational Photography | ||
or CIS 5810 | Computer Vision & Computational Photography | |
| Deep Learning: A Hands-on Introduction | ||
| TinyML: Tiny Machine Learning for Embedded Systems | ||
| Control For Autonomous Robots | ||
| Scalable and Cloud Computing | ||
| Crowdsourcing and Human Computation | ||
| AI Electives | ||
| Remaining course units from any of the six categories above, or any of the following: | ||
| Machine Learning Electives | ||
| Mathematics of Machine Learning | ||
| Advanced Topics in Machine Learning | ||
| Theory of Machine Learning | ||
| Machine Learning for Time-Series Data | ||
or ESE 5380 | Machine Learning for Time-Series Data | |
| Graph Neural Networks | ||
| Principles of Deep Learning | ||
| Deep Generative Models | ||
| Information Theory | ||
| Optimization, Systems, and Control Electives | ||
| Stochastic Systems Analysis and Simulation | ||
| Linear Systems Theory | ||
| Feedback Control Design and Analysis | ||
| Introduction to Optimization Theory | ||
| Modern Convex Optimization | ||
| Combinatorial Optimization | ||
| Learning for Dynamics and Control | ||
| Model Predictive Control | ||
| Other AI Electives | ||
| Brain-Computer Interfaces | ||
| Introduction to Human Computer Interaction | ||
or CIS 5120 | Introduction to Human Computer Interaction | |
| Database and Information Systems | ||
or CIS 5500 | Database and Information Systems | |
| Fundamentals of Computational Biology | ||
| Machine Perception | ||
| Advanced Topics in Databases | ||
| Introduction to Robotics | ||
| Advanced Robotics | ||
| Engineering Markets | ||
| F1/10 Autonomous Racing Cars | ||
| Learning in Robotics | ||
| Theory of Networks | ||
| Algorithmic Game Theory | ||
| Senior Design | ||
| CIS 4000 | Senior Project | 1 |
| or ESE 4500 | Senior Design Project I - EE and SSE | |
| or MEAM 4450 | Mechanical Engineering Design Projects | |
| or BE 4950 | Senior Design Project | |
| or MSE 4950 | Senior Design | |
| or CBE 4000 | Introduction to Product and Process Design | |
| CIS 4010 | Senior Project | 1 |
| or ESE 4510 | Senior Design Project II - EE and SSE | |
| or MEAM 4460 | Mechanical Engineering Design Projects | |
| or BE 4960 | Senior Design Project | |
| or MSE 4960 | Senior Design | |
| or CBE 4590 | Product and Process Design Projects | |
| Technical Electives | ||
| Three course units from Engineering, Math or Natural Science or listed at https://advising.cis.upenn.edu/tech-electives 2 | 3 | |
| General Electives 3 | ||
| AI Ethics Elective | ||
| CIS 4230 | Ethical Algorithm Design | 1 |
| or CIS 5230 | Ethical Algorithm Design | |
| or LAWM 5060 | ML: Technology Law | |
| Cognitive Science Elective | ||
| Select one of the following Cognitive Science electives: | 1 | |
| Introduction to Cognitive Science | ||
| Introduction to Formal Linguistics | ||
| Introduction to Syntax | ||
| Semantics I | ||
| Introduction to Logic | ||
| Introduction to Philosophy of Mind | ||
| Logic and Computability 1 | ||
| Philosophy of Psychology | ||
| Introduction to Brain and Behavior | ||
| Perception | ||
| Cognitive Neuroscience | ||
| Language and Thought | ||
| Judgment and Decisions | ||
| Select 3 Social Science or Humanities courses | 3 | |
| Select 2 Social Science or Humanities or Technology in Business & Society courses | 2 | |
| Free Elective | ||
| Select 1 course unit of free elective. | 1 | |
| Total Course Units | 37 | |
- 1
The Natural Science elective can be satisfied with appropriate AP credits, e.g., AP Physics. (a list of approved Natural Science course can be found on the SEAS UG Handbook)
- 2
May contain at most one CU of 1000-level courses.
- 3
Must include a Writing Seminar (a list of approved Writing Seminars can be found in the SEAS Undergraduate Handbook).
The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2024 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.