We're still actively developing this site. If you encounter any issues, please report them! - Report an issue

INTRODUCTION TO ARTIFICIAL INTELLIGENCE

COMPSCI 540
Course Description

Principles of knowledge-based search techniques, automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning. Applications in tasks such as problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics.

Prerequisties

(COMPSCI 300 , COMPSCI 320 or 367) and (MATH 211 , MATH 217 , MATH 221 , or 275) or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for Professionals

Satisfies

This course does not satisfy any prerequisites.

Credits

3

Offered

Fall, Spring, Summer

Grade Point Average
3.41

6.43% from Historical

Completion Rate
99.22%

3.22% from Historical

A Rate
35.47%

4.29% from Historical

Class Size
516

127.63% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS

COMPSCI/ECE/ME 539

Theory and applications of artificial neural networks: multi-layer perceptron, self-organization mapdeep neural network convolutional neural network, recurrent network, support vector machines genetic algorithm, and evolution computing. Applications to control, pattern recognition, prediction, and object detection and tracking.

INTRODUCTION TO THEORY OF COMPUTING

COMPSCI 520

Basics about the notion, capabilities, and limitations of computation: elements of finite automata and regular languages, computability theory, and computational complexity theory. Additional topics include context-free grammars and languages, and complexity-theoretic cryptography.

MACHINE LEARNING

COMPSCI/ECE 760

Computational approaches to learning: including inductive inference, explanation-based learning, analogical learning, connectionism, and formal models. What it means to learn. Algorithms for learning. Comparison and evaluation of learning algorithms. Cognitive modeling and relevant psychological results.

ADVANCED NATURAL LANGUAGE PROCESSING

COMPSCI 769

Develop algorithms and mathematical models for natural language processing tasks, including text categorization, information retrieval, speech recognition, machine translation, and information extraction. Focus is on the state-of-the-art computational techniques as they are applied to natural language tasks. Students are strongly encouraged to have knowledge of introductory artificial intelligence (e.g.,COMPĀ SCIĀ 540).

PROBLEM SOLVING USING COMPUTERS

COMPSCI 310

Gives students an introduction to computer and analytical skills to use in their subsequent course work and professional development. Discusses several methods of using computers to solve problems, including elementary programming techniques, symbolic manipulation languages, and software packages. Techniques will be illustrated using sample problems drawn from elementary engineering. Emphasis is on introduction of algorithms with the use of specific tools to illustrate the methods.