CS 3600 - Introduction to Artificial Intelligence
CS 3600: Introduction to Artificial Intelligence is a 3-credit Computer Science course. It focuses on the theory and implementation of intelligent artificial agents. It is a required course in the Intelligence thread of the Computer Science major or minor.
Overview[edit | edit source]
Coursework includes programming projects in Python and exams.
Topics include:[1]
- Graph search
- Uninformed search: BFS, DFS, Dijkstra's algorithm/uniform cost search
- Informed search: A*
- Markov decision processes
- Q-learning
- Probability review
- Bayesian networks
- Partially observable Markov decision processes
- Optimization
- Machine learning
- Supervised learning
- Decision trees
- Linear regression and classification
- Artificial neural networks and deep learning
- Supervised learning
The textbook used is Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig. The textbook is not required.
CS 3600 is heavily based on UC Berkeley's open course CS 188: Introduction to Artificial Intelligence.
The official prerequisite for the course is CS 1332. It is helpful to have prior experience in Python programming and basic probability, as well as perhaps linear algebra, calculus, and computational complexity.[1]
Taking this course is helpful before taking CS 4641: Machine Learning or CS 3630: Robotics and Perception, which are comparatively harder courses, but it is not a prerequisite.
Resources[edit | edit source]
- Syllabus (Spring 2019)
- Course notes from the UC Berkeley version of the course – these have more detail than what's necessary for CS 3600
