INTRODUCTION TO ARTIFICIAL INTELLIGENCE
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.
Prerequisites & course information
Requisites: ( COMP SCI 300 or 320 ), ( MATH 320 , 340 , 341 , 345 , 375 or M E/COMP SCI/E C E 532 ), and ( STAT 311 , 324 , 333 , 340 , 371 , MATH/STAT 309 , 431 , MATH 331 , 531 , or E C E 331 ), or grad/professional standing, or declared in Capstone Cert in Computer Science for Prof Course Designation: Core GenEd - Mathematics & Quantitative Reasoning Breadth - Natural Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Identify, formulate and solve AI problems using appropriate computational approaches. Audience: Undergraduate 2. Apply knowledge of computer science, mathematics, and statistics to the analysis and enhancement of AI systems Audience: Undergraduate 3. Design and conduct AI experiments, and analyze and interpret data and outcomes. Audience: Undergraduate 4. Demonstrate their awareness of major theories, approaches, concepts, and current and classical research findings in AI. Audience: Undergraduate 5. Examine professional and ethical responsibility in AI practices for society. Audience: Undergraduate Learning Outcomes: 1. Identify, formulate and solve AI problems using appropriate computational approaches. Audience: Undergraduate 2. Apply knowledge of computer science, mathematics, and statistics to the analysis and enhancement of AI systems Audience: Undergraduate 3. Design and conduct AI experiments, and analyze and interpret data and outcomes. Audience: Undergraduate 4. Demonstrate their awareness of major theories, approaches, concepts, and current and classical research findings in AI. Audience: Undergraduate 5. Examine professional and ethical responsibility in AI practices for society. Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027