Computer Science Major (BS/BA)
CS Course Requirements
CS Breadth: Artificial Intelligence

The courses below fulfill the Breadth: Artificial Intelligence requirement in computer science.

EECS 325 - Artificial Intelligence Programming

Introduction to Lisp and programming knowledge-based systems and interfaces. Strong emphasis on writing maintainable, extensible systems. Topics include: semantic networks, frames, pattern matching, deductive inference rules, case-based reasoning, discrimination trees. Project-driven. Substantial programming assignments.

EECS 337 - Natural Language Processing

A semantics-oriented introduction to natural language processing, broadly construed. Representation of meaning and knowledge inference in story understanding, script/frame theory, plans and plan recognition, counter-planning, and thematic structures. This course satisfies the project requirement

EECS 344 - Design of Problem Solvers

Principles and practice of organizing and building AI reasoning systems. Topics include pattern-directed rule systems, truth-maintenance systems, and constraint languages. This course satisfies the project requirement.

EECS 348 - Intro to Artificial Intelligence

Core techniques and applications of artificial intelligence. Representation retrieving and application of knowledge for problem solving. Hypothesis exploration, theorem proving, vision and neural networks.

EECS 349 - Machine Learning

Machine Learning is the study of algorithms that improve automatically through experience. Topics covered typically include Bayesian Learning, Decision Trees, Genetic Algorithms, Neural Networks.

EECS 371 - Knowledge Representation and Reasoning

Principles and practices of knowledge representation, including logics, ontologies, common sense knowledge, and semantic web technologies. Prerequisite: 3EECS 348, EECS 325, or equivalent experience with artificial intelligence. This course satisfies the project requirement.

EECS 372, 472 - Designing and Constructing Models with Multi-Agent Languages

Joint with EECS 372. This course focuses on the exploration, construction and analysis of multi-agent models. Sample models from a variety of content domains are explored and analyzed. Spatial and network topologies are introduced. The prominent agent-based frameworks are covered as well as methodology for replicating, verifying and validating agent-based models. We use state of the art ABM and complexity science tools. This course can help satisfy the project course and artificial intelligence area course requirement for CS and CIS majors, and satisfy the breadth requirement in artificial intelligence for Ph.D. students in CS. It also satisfies a design course requirement for Learning Sciences graduate students, counts towards the Cognitive Science specialization and as an advanced elective for the Cognitive Science major.