The AI minor aims to introduce students to both technical and societal issues associated with artificial intelligence, and provides students with exposure to some of the mathematical and algorithmic underpinnings of the field — including problem solving and machine learning.  Students will be introduced to applications of AI in areas as diverse as computer vision, speech recognition and language understanding, robotics, human-AI interaction, and engineering. They’ll also get a taste of ethical, policy and economic issues that arise from the growth of AI, as well as the connections between artificial and human intelligence. The AI minor is designed to be widely accessible to CMU students who have an appropriate background in math and programming.

Note: The AI minor is not available to SCS students, nor is there an AI concentration. Instead, SCS students can take a concentration in related areas, including machine learning, robotics, language technologies and human-computer interaction.

What You'll Learn

Students who minor in AI will:

• Understand how to distill a real-world challenge into an artificial intelligence problem.
• Understand, implement, and use state-of-the art AI and machine learning techniques for dealing with real-world problems.
• Design AI systems that can learn from and interact effectively with people.
• Analyze the commercial and societal impact of AI technologies and understand the underlying responsibility to consider the ethical, privacy, moral and legal implications of AI technologies.

Requirements

The minor consists of six courses, including three courses in the AI core, two technical electives and one elective in societal aspects of AI.

Prerequisites

• Principles of Imperative Computation: 15‐122 (10 units)
• Calculus II: 21‐112, 21‐120 or 21‐259 (10 units)
• Concepts of Mathematics: 21‐127, 21‐128 or 15‐151 (10 units)
• Matrix Algebra: 21‐240 or 21-241 (10 units)

Required Core Courses

• Probability: one of 36-225, 21-325, 36-218 or 15-259 (9 units)
• AI: Representation and Problem Solving: 15-281 (12 units)
• Introduction to Machine Learning: 10-301 or 10-315 (12 units)

Technical Electives

Students are required to take an approved course from two of the following three cluster areas. Each course must be a minimum of 9 units.

Cognition and Action Cluster

• 15-386: Neural Computation
• 15-482: Autonomous Agents
• 15-494: Cognitive Robotics
• 16-350: Planning Techniques for Robotics
• 16-362: Mobile Robot Programming Laboratory
• 16-384: Robot Kinematics and Dynamics
• 85-213: Human Information Processing and AI
• 85-412: Cognitive Modeling
• 85-419: Introduction to Parallel Distributed Processing
• 85-435: Biologically Intelligent Exploration

Machine Learning Cluster

• 10-403: Deep Reinforcement Learning & Control
• 10-414: Deep Learning Systems
• 10-417: Intermediate Deep Learning
• 10-418: Machine Learning for Structured Data
• 11-441: Machine Learning for Text Mining
• 11-485: Introduction to Deep Learning
• 15-388/67-364: Practical Data Science
• 36-401: Modern Regression
• 36-402: Advanced Data Analysis

Perception and Language Cluster

• 11-411: Introduction to Natural Language Processing
• 11-442: Search Engines
• 11-492: Speech Processing
• 15-387: Computational Perception
• 15-463: Computational Photography
• 16-385: Computer Vision
• 85-370: Perception
• 85-345: Meaning in Mind and Brain
• 85-408: Visual Cognition

Societal Aspects of AI

Students are required to take an approved course from one of the following two cluster areas. Each course must be a minimum of 9 units. (Note: two minis can be combined to form one 9-unit course.)

Human-AI Interaction Cluster

• 05-317: Design of Artificial Intelligence Products
• 05-318: Human-AI Interaction
• 05-391: Designing Human-Centered Systems
• 16-467: Human-Robot Interaction

AI and Humanity Cluster

• 16-735: Ethics and Robotics
• 17-200: Ethics and Policy Issues in Computing
• 79-302: Killer Robots (mini)
• 80-249: AI, Society and Humanity
• 88-230: Human Intelligence and Human Stupidity
• 88-275: Bubbles: Data Science for Human Minds
• 88-380: Dynamic Decisions
• 90-442: Critical AI Studies for Public Policy (mini)
• 94-441: Ethics and Politics of Data (mini)

Double Counting

At most two courses in the AI minor may be double-counted with any other major or minor.

To Apply

Apply for an Additional Major or Minor
Complete our application including a statement (maximum one page) of why you want to take the minor and how it fits into your career goals. Students must have all prerequisites completed and 15-281 or 10-301/10-315, while maintaining a "C" average in aforementioned courses. 

Students must apply for admission no later than the semester before they intend to graduate. An admission decision will usually be made within one month. Students are encouraged to apply as early as possible in their undergraduate careers so the advisor of the AI minor can provide advice on their curriculum. Applications can be accepted based on midterm grades.