The AI degree provides the mathematical and algorithmic foundations of AI techniques, along with hands-on experience in programming as well as using AI tools and foundation models. Complementing these engineering skills with a broader perspective, students learn about intelligence from a cognitive science perspective, and they develop a sense of the issues (and solutions) required to responsibly develop AI to benefit society.  Finally, students choose a concentration, ranging from machine learning, to vision and language, to data and society, to robotics, to AI and health systems. Interested students can learn more about the courses below by visiting the Penn Course Catalog.

Mathematics and Natural Science (7 CUs):

  • MATH 1400: Calculus, Part I
  • MATH 1410: Calculus, Part II
  • CIS 1600: Mathematics of Computer Science
  • ESE 2030: Linear Algebra with Applications to Engineering and AI
  • ESE 3010: Probability or STAT 4300
  • ESE 4020: Statistics for Data Science
  • Natural science [1 CU, no lab requirement]

Computing (5 CUs):

  • CIS 1100: Python Programming
  • CIS 1200: Programming Languages
  • CIS 1210: Data Structures
  • CIS 3200: Algorithms
  • CIS 2450: Big Data 

AI (12 CUs):

Students choose at least one course unit from each of the following six categories:

Introduction to AI
  • ESE 2000: Data, Systems, Decisions
  • CIS 4210: Introduction to Artificial Intelligence
Machine Learning
  • CIS 4190: Applied Machine Learning
  • CIS 5200: Machine Learning
Signals & Systems
  • ESE 2100: Dynamic Systems
  • ESE 2240: Signal and Information Processing
Optimization & Control
  • ESE 3040 Optimization
  • ESE 4210 Control For Autonomous Robots
Vision & Language
  • CIS 5810: Computer Vision & Computational Photography
  • CIS 5300: Natural Language Processing
AI Project (must have AI development, 30% of grade from term project)
  • ESE 3060: Deep Learning: A Hands-on Introduction
  • CIS 3500: Software Engineering
  • ESE 3600: Tiny Machine Learning
  • ESE 4210: Control for Autonomous Robots
  • CIS 5810: Computer Vision & Computational Photography
  • CIS 5300: Natural Language Processing
  • NETS 2120: Scalable and Cloud Computing
  • NETS 2130: Crowd Sourcing and Human Computation

AI Electives:

In addition to the courses above, students will have an opportunity to take six AI courses selected from the list of approved courses below, along with the 1-year senior design sequence:

Machine Learning Electives
  • CIS 3333: Mathematics of Machine Learning
  • ESE 5460: Principles of Deep Learning
  • ESE 5140: Graph Neural Networks
  • ESE 4380: Machine Learning for Time-Series Data
  • ESE 6450: Deep Generative Models
  • CIS 6200: Advanced Deep Learning
  • CIS 6250: Computational Learning Theory
  • ESE 6740: Information Theory
  • CIS 7000: Trustworthy AI
Optimization, Systems, and Control Electives
  • ESE 3030: Stochastic Systems Analysis and Simulation
  • ESE 5000: Linear Systems Theory
  • ESE 5050: Control Systems
  • ESE 5060: Linear Optimization
  • ESE 6050: Modern Convex Optimization
  • ESE 6060: Combinatorial Optimization
  • ESE 6190: Model Predictive Control
  • ESE 6180: Learning for Dynamics and Control
Other AI Electives
  • MEAM 5200: Robotics
  • MEAM 6200: Advanced Robotics
  • ESE 6500: Learning in Robotics
  • ESE 6150: F1/10 Autonomous Racing Cars
  • CIS 4120: Human-Computer Interaction
  • CIS 5800: Machine Perception
  • CIS 5360: Computational Biology
  • BE 5210: Brain Computer Interfaces
  • CIS 4500: Databases
  • CIS 6500: Advanced Topics Databases
  • CIS 3990: Wireless and Mobile Sensing
  • NETS 3120: Theory of Networks
  • NETS 4120: Algorithmic Game Theory
  • ESE 4040: Engineering Markets

The above list will evolve as new courses are added to the program.

AI Concentrations:

The seven AI elective courses can be structured along AI concentrations depending on the interests of the student. Concentrations are optional and consist of four courses in a specific theme. 

  • Robotics
  • Vision/Language
  • Machine Learning
  • Data/Society
  • Health/Systems

Senior Design (2 CU):

Rather than offering a specific course for senior design, AI majors will embed themselves into the ESE, CIS or other Penn Engineering senior design courses. This will enable AI students to apply their AI skills across many engineering challenges.

Technical Electives (3 CU):

Three course units from Engineering, Math, Natural Science or from the list here.

General Electives (8 CUs):

AI Ethics: Choose at least one of the following
  • CIS 4230: Ethical Algorithm Design
  • LAWM 5060: Machine Learning: Technology Law
Cognitive Science Elective: Choose at least one of the following
  • COGS 1001: Intro to Cognitive science
  • LING 0500: Introduction to Formal Linguistics
  • LING 2500: Introduction to Syntax
  • LING 3810: Semantics I
  • PSYC 1210: Introduction to Brain and Behavior
  • PSYC 1340: Perception
  • PSYC 1230: Cognitive Neuroscience
  • PSYC 1310: Language and Thought
  • PSYC 2737: Judgment and Decisions
  • PHIL 1710: Introduction to Logic
  • PHIL 2640: Introduction to Philosophy of Mind
  • PHIL 4721: Logic and Computability 1
  • PHIL 4840: Philosophy of Psychology

  • SS/H Electives: Five course units, including a writing course. Three of these courses must be Social Science or Humanities courses, and 2 can be Social Science, Humanities, or Technology in Business & Society courses.
  • Free Elective: One course unit from free electives.