Course Description

CSC 114: Artificial Intelligence I

This course covers the study of intelligent agent design and rational decision making. Topics include goal-driven agents, search techniques, optimization, basic problem-solving methods, logic, knowledge-based agents, statistical and probabilistic reasoning, and the basics of machine learning. Upon completion, students should be able to demonstrate artificial intelligence design concepts.

What You Will Study

This course moves through eight interconnected topics over eight weeks:

  1. Foundations of AI — The history, definitions, and societal context of artificial intelligence

  2. Intelligent Agents — How AI systems perceive their environment and select actions

  3. Search Techniques — Algorithms for finding solutions in large problem spaces

  4. Optimization and CSPs — Local search, simulated annealing, and constraint satisfaction

  5. Logic — Propositional logic and formal reasoning for knowledge representation

  6. Knowledge-Based Agents — Inference rules, chaining strategies, and expert systems

  7. Probability and Uncertainty — Probabilistic reasoning and Bayesian networks

  8. Machine Learning Foundations — Supervised learning, classification, and model evaluation

AI is not a single algorithm — it is a discipline that combines ideas from mathematics, computer science, cognitive science, and philosophy to build systems that can reason and act effectively in the world.

Prerequisites

Students should have completed an introductory programming course or have equivalent programming experience. Comfort with basic algebra and logic is helpful but not required; these skills are developed within the course.

Course Format

This is an online asynchronous course. Each week’s materials open on Sunday and include readings, instructional videos, quizzes, discussions, and hands-on lab assignments. See the Weekly Schedule for due dates.

This work is licensed under CC BY-SA 4.0.