Courses
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Computer Science Curriculum
CS Depth: Artificial Intelligence

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

EECS 301 - Introduction to Robotics Laboratory

A laboratory-based introduction to robotics. Focus will be on both hardware (sensors and actuators) and software (sensor processing and behavior development). Topics will include: the basics in kinematics, dynamics, control, and motion planning; and an introduction to Artificial Intelligence (AI) and Machine Learning (ML). Formerly EECS 295. This course fulfills the AI Depth requirement.

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 - Intro to Semantic Information 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 360 - Introduction to Feedback Systems

Linear feedback control systems, their physical behavior, dynamical analysis, and stability. Laplace transform, frequency spectrum, and root locus methods. System design and compensation using PID and lead-lag controllers. Digital implementations of analog controllers.

EECS 371 - Knowledge Representation and Reasoning

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

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

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.

EECS 395-495 - (Special Topics) Innovation in Journalism & Technology

This is a joint projects class with Medill in conjunction with the newly announced Knight News Innovation Lab at Northwestern. McCormick students (primarily CS and CE majors) and journalism students will join cross-functional teams to assess and develop, from both an audience/market perspective and a technology perspective, a range of technology projects with the ultimate goal of deployment for impact in media and journalism. Some projects may continue over the summer if students are interested.

EECS 395-495 - Building Next-Generation AI Applications with IBM Watson

Students will build new intelligent applications using IBM's Watson. IBM will work with us to provide the base technology and some technical advice, as well as guest lecturers etc. This is a joint class with the IEMS & the MS Analytics program.

EECS 395-495 - Cognitive Simulation for Virtual Characters

TBA

EECS 395-495 - Computational Geometry

After a brief introduction to numerical computation issues, the course will continue with a sequence of canonical problem settings (e.g., Intersections; Arrangements/Duality), mostly focusing on the combinatorial aspects of the algorithms and the impact of the data structures. Each part will be casted in respective applications settings (GIS; Motion Planning; etc). The last part of the course will present several potpourri-like topics, e.g., Skeletons/Medial Axis; Davenport-Shinzel sequences.

EECS 395-495 - Knowledge, Representation & Reasoning for Game Characters

This course will explore the use of formal knowledge representation and reasoning methods from artificial intelligence in the use of an experimental computer game. Topics include logic programming and the Prolog language, knowledge representation, planning and action selection, and simple natural language dialog.

EECS 395-495 - Probabilistic Graphical Models

Probabilistic graphical models are a powerful technique for handling uncertainty in machine learning. The course will cover how probability distributions can be represented in graphical models, how inference and learning are performed in the models, and how the models are utilized for machine learning in practice.