Artificial Intelligence: Representation and Problem Solving
Course ID 15281
Description This course is about the theory and practice of Artificial Intelligence. We will study modern techniques for computers to represent task-relevant information and make intelligent (i.e. satisficing or optimal) decisions towards the achievement of goals. The search and problem solving methods are applicable throughout a large range of industrial, civil, medical, financial, robotic, and information systems. We will investigate questions about AI systems such as: how to represent knowledge, how to effectively generate appropriate sequences of actions and how to search among alternatives to find optimal or near-optimal solutions. We will also explore how to deal with uncertainty in the world, how to learn from experience, and how to learn decision rules from data. We expect that by the end of the course students will have a thorough understanding of the algorithmic foundations of AI, how probability and AI are closely interrelated, and how automated agents learn. We also expect students to acquire a strong appreciation of the big-picture aspects of developing fully autonomous intelligent agents. Other lectures will introduce additional aspects of AI, including natural language processing, web-based search engines, industrial applications, autonomous robotics, and economic/game-theoretic decision making.
Key Topics
Throughout the course, we will discuss topics such as AI and Ethics and introduce applications related to AI for Social Good.
Learning Resources
Artificial Intelligence: A Modern Approach, Third Edition (recommended), Piazza, Gradescope
Course Relevance
This 15-281 course is for undergraduates.
15-281 used to be 15-381 in previous semesters. This is not a significant change to the course, but rather a recognition that many students are able to complete the necessary prerequisites and are prepared to take this course in their second year.
Course Goals
We expect that by the end of the course students will have a thorough understanding of the algorithmic foundations of AI, how probability and AI are closely interrelated, and how automated agents make decisions. We also expect students to acquire a strong appreciation of the big-picture aspects of developing fully autonomous intelligent agents.
Pre-required Knowledge
The prequisites for this course are:
15-122 Principles of Imperative Computation
21-241 Matrices and Linear Transformations
21-127 Concepts of Mathematics or 15-151 Mathematical Foundations of Computer Science.
The corequisite for this course is:
21-122 Integration and Approximation
For this corequisite, you should either have completed it prior to starting 15-281 or have it on your schedule for Fall 2019.
Please see the instructors if you are unsure whether your background is suitable for the course.
Assessment Structure
Grades will be collected in Canvas.
Midterms 15% (each), Final 30%, Programming homework 20%, Written homework 10%, Online homework 5%, Participation 5%
Course Link
https://www.cs.cmu.edu/~15281/