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.
The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2026 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.
Curriculum
Artificial Intelligence (ARIN) Major Requirements
| Code | Title | Course Units |
|---|---|---|
| Computing | ||
| CIS 1100 or CIS Elective 1 | 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 | Calculus for the Mathematical Sciences | |
| 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 2 | 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 | ||
| Introduction to 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 | ||
| Trustworthy Machine Learning | ||
| 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 | ||
| AI for Science and Engineering: From Data to Discovery | ||
| Introduction to Robotics | ||
| Advanced Robotics | ||
| Learning and Control for Adaptive and Reactive Robots | ||
| Engineering Markets | ||
| RoboRacer Autonomous Racing Cars | ||
| Learning in Robotics | ||
| Physical Intelligence: Science and Systems | ||
| Theory of Networks | ||
| Algorithmic Game Theory | ||
| Senior Design | ||
| CIS 4000 | Senior Project | 1 |
| or CIS 4100 | CIS Senior Thesis | |
| 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 | |
| CIS 4010 | Senior Project | 1 |
| or CIS 4110 | CIS Senior Thesis | |
| 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 | |
| Technical Electives 3 | ||
| Select three total course units from the following: | 3 | |
CIS Restricted or Unrestricted Technical Electives 4 | ||
| General Electives 5 | ||
| AI Ethics Elective | ||
| LAWM 5060 | ML: Technology Law 6 | 1 |
| or CIS 4230 | Ethical Algorithm Design | |
| or CIS 5230 | Ethical Algorithm Design | |
| 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 7 | ||
| Select 1 course unit of free elective | 1 | |
| Total Course Units | 37 | |
- 1
A CIS Elective is a CIS or NETS engineering course at the 1000 level or above, (NOTE: not all CIS/NETS courses are engineering courses; please see the SEAS Undergraduate Handbook. At most, one CU of 1000-level coursework may be used as a CIS Elective.
- 2
The Natural Science elective can be satisfied with appropriate AP credits, e.g., AP Physics or by taking a 1 CU course from the approved list of SEAS Natural Science Courses (EUNS).
- 3
May contain at most one CU of 1000-level courses.
- 4
Must be from the list of approved courses.
- 5
Must include a Writing Seminar from the list below:
WRIT 0020 (H), WRIT 0021 (SS), WRIT 0100 (H),WRIT 0110 (H), WRIT 0120 (H), WRIT 0130 (H), WRIT 0140 (H), WRIT 0150 (H), WRIT 0160 (SS), WRIT 0170 (SS), WRIT 0220 (TBS), WRIT 0230 (H), WRIT 0250 (H), WRIT 0260 (H), WRIT 0270 (H), WRIT 0280 (SS),WRIT 0300 (H), WRIT 0310 (TBS), WRIT 0320 (-), WRIT 0330 (H), WRIT 0340 (SS), WRIT 0370 (SS), WRIT 0380 (SS), WRIT 0390 (H), WRIT 0400 (TBS), WRIT 0410 (H), WRIT 0480 (SS), WRIT 0490 (H), WRIT 0500 (SS), WRIT 0550 (SS), WRIT 0570 (H), WRIT 0580 (H), WRIT 0590 (SS),WRIT 0650 (TBS), WRIT 0670 (H), WRIT 0680 (H), WRIT 0730 (H), WRIT 0740 (TBS)- 6
Only the "Technology Law and Ethics" section satisfies the AI Ethics requirement.
- 7
Penn Engineering undergraduates may not use courses on this list toward their degree:
- ASTR 0001
- CHEM 1011
- CIS (CSE)
- EAS 5030 (SEAS UG students may register for EAS 4030 for TBS credit)
- EAS 5050 (SEAS UG students may register for EAS 3010 for TBS credit)
- Education (inter-session courses)
- MATH 1300, MATH 1700
- MCIT courses
- Military Science
- Naval Science (except NSCI 1020, NSCI 2010 , NSCI 2020, NSCI 3010, NSCI 4010, NSCI 4020)
- Organizational Dynamics (DYNM) courses
- PHYS 1100, below PHYS 0140 (except PHYS 0050, PHYS 0051)
- Statistics below 4300 (Note: effective Fall 2021 STAT 4050 and STAT 4220 can only be used as free electives)
- Perelman School of Medicine (MED courses)
- Wharton Global Youth Pre-Baccalaureate courses
- The Green Program (TGP) study abroad
Students may select one concentration.
Machine Learning
| Code | Title | Course Units |
|---|---|---|
| Choose 4 courses: 1 | 4 | |
| Machine Learning for Time-Series Data | ||
| Graph Neural Networks | ||
| Principles of Deep Learning | ||
| Advanced Topics in Machine Learning | ||
| Theory of Machine Learning | ||
| Total Course Units | 4 | |
Robotics
| Code | Title | Course Units |
|---|---|---|
| Choose 4 courses: 1 | 4-4.5 | |
| Control For Autonomous Robots | ||
| Introduction to Robotics | ||
| RoboRacer Autonomous Racing Cars | ||
| Advanced Robotics | ||
| Learning and Control for Adaptive and Reactive Robots | ||
| Learning in Robotics | ||
| Total Course Units | 4-4.5 | |
Vision & Language
| Code | Title | Course Units |
|---|---|---|
| Choose 4 courses 1 | 4 | |
| Computer Vision & Computational Photography | ||
| Natural Language Processing | ||
| Machine Perception | ||
| Advanced Topics in Machine Perception | ||
| Advanced Topics in Natural Language Processing | ||
| Deep Generative Models | ||
| Total Course Units | 4 | |
- 1
Students can pursue an optional concentration by selecting 4 of their required 6 AI electives courses from the list of approved courses for the concentration of their choice. Note that only courses taken towards the AI Elective category are eligible to count for a concentration
| First Year | ||
|---|---|---|
| Fall | Course Units | |
| ESE 2000 | Artificial Intelligence Lab: Data, Systems, and Decisions | 1 |
| CIS 1100 | Introduction to Computer Programming | 1 |
| MATH 1400 | Calculus, Part I | 1 |
| Natural Science Elective | 1 | |
| Course Units | 4.00 | |
| Spring | ||
| CIS 1200 | Programming Languages and Techniques I | 1 |
| CIS 1600 | Mathematical Foundations of Computer Science | 1 |
| MATH 1410 | Calculus, Part II | 1 |
| General Elective: WRIT 0001-0091 with SS (EUSS), H (EUHS), or TBS (EUTB) | 1 | |
| General Elective: SS or H | 1 | |
| Course Units | 5.00 | |
| Second Year | ||
| Fall | ||
| ESE 2030 | Linear Algebra with Applications to Engineering and AI | 1 |
| CIS 1210 | Programming Languages and Techniques II | 1 |
| CIS 2450 | Big Data Analytics | 1 |
| ESE 2100 or ESE 2240 | Introduction to Dynamic Systems or Signal and Information Processing | 1 |
| General Elective: SS or H | 1 | |
| Course Units | 5.00 | |
| Spring | ||
| ESE 3010 or STAT 4300 | Engineering Probability or Probability | 1 |
| LAWM 5060 | ML: Technology Law or Ethical Algorithm Design or Ethical Algorithm Design | 1 |
| AI Elective 4 | 1 | |
| Technical Elective 6 | 1 | |
| AI Project 5 | 1 | |
| Course Units | 5.00 | |
| Third Year | ||
| Fall | ||
| CIS 3200 | Introduction to Algorithms | 1 |
| ESE 3040 or ESE 4210 | Introduction to Optimization or Control For Autonomous Robots | 1 |
| ESE 4020 or ESE 5420 | Statistics for Data Science or Statistics for Data Science | 1 |
| CIS 4300 | Natural Language Processing or Natural Language Processing or Computer Vision & Computational Photography or Computer Vision & Computational Photography | 1 |
| General Elective: SS or H or TBS 7 | 1 | |
| Course Units | 5.00 | |
| Spring | ||
| CIS 4190 | Applied Machine Learning or Applied Machine Learning or Machine Learning | 1 |
| AI Elective | 1 | |
| AI Elective | 1 | |
| Cognitive Science Elective | 1 | |
| Free Elective | 1 | |
| Course Units | 5.00 | |
| Fourth Year | ||
| Fall | ||
| CIS 4000 | Senior Project or CIS Senior Thesis or Senior Design Project I - EE and SSE or Mechanical Engineering Design Projects or Senior Design Project or Senior Design | 1 |
| AI Elective | 1 | |
| AI Elective | 1 | |
| Technical Elective | 1 | |
| Course Units | 4.00 | |
| Spring | ||
| CIS 4010 | Senior Project or CIS Senior Thesis or Senior Design Project II - EE and SSE or Mechanical Engineering Design Projects or Senior Design Project or Senior Design | 1 |
| AI Elective | 1 | |
| Technical Elective | 1 | |
| General Elective: SS or H or TBS | 1 | |
| Course Units | 4.00 | |
| Total Course Units | 37.00 | |
- 1
The Natural Science elective can be satisfied with appropriate AP credits, e.g., AP Physics or by taking a 1 CU course from the approved list of SEAS Natural Science Courses (EUNS).
- 2
A list of approved Writing Seminars can be found in the SEAS Undergraduate Handbook.
- 3
Must be from the list of SEAS Social Science (EUSS) or SEAS Humanities (EUHS) courses.
- 4
Must be from the approved list of AI Electives.
- 5
Must be from the approved list of AI Project courses.
- 6
Must be from one of the following approved lists: SEAS Engineering (EUNG), CIS Restricted Technical Electives (EUCR), and CIS Unrestricted Technical Electives (EUCU). Must contain at most 1 CU of 1000-level courses.
- 7
At most 2 CUs of Technology, Business & Society (EUTB) courses can be taken towards the General Electives requirement.