CSC 114: Artificial Intelligence I

Welcome to CSC 114: Artificial Intelligence I at Central Piedmont Community College. This course explores how computers can be designed to reason, learn, and act intelligently. From search algorithms to machine learning, you will build a solid foundation in the core ideas that power modern AI systems.

Course Description

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.

AI is not a single technology — it is a set of ideas and techniques for building systems that perceive their environment and take actions to achieve goals. Every unit in this course contributes one more tool to that toolkit.

Course Information

Unit Overview

This course is organized into eight weekly units. Each unit builds on the previous one, moving from the foundations of what AI is, through the techniques agents use to solve problems, to the probabilistic and machine learning methods that power today’s applications.

Unit	Topic
Unit 1	Foundations of Artificial Intelligence — What is AI, where did it come from, and what can it do? An introduction to the field, its history, and its societal implications.
Unit 2	Intelligent Agents — How do we model an AI system as an agent that perceives its environment and selects actions? The PEAS framework, environment types, and agent architectures.
Unit 3	Search Techniques for Problem Solving — Formulating problems as state-space search, uninformed strategies (BFS, DFS, UCS), and informed heuristic search (greedy best-first, A*).
Unit 4	Optimization and Constraint Satisfaction — Local search, simulated annealing, genetic algorithms, and constraint satisfaction problems (CSPs) with backtracking and heuristics.
Unit 5	Introduction to Logic — Propositional logic, logical equivalences, validity, and why formal reasoning matters for AI knowledge representation.
Unit 6	Knowledge-Based Agents and Inference — Building knowledge bases, inference rules, forward and backward chaining, Horn clauses, and expert systems.
Unit 7	Probability and Uncertainty — Probability fundamentals, conditional probability, Bayes' theorem, Bayesian networks, and reasoning under uncertainty.
Unit 8	Machine Learning Foundations — What is machine learning? Supervised learning, model evaluation, bias-variance tradeoff, and the current AI landscape.

Unit

Topic

Unit 1

Foundations of Artificial Intelligence — What is AI, where did it come from, and what can it do? An introduction to the field, its history, and its societal implications.

Unit 2

Intelligent Agents — How do we model an AI system as an agent that perceives its environment and selects actions? The PEAS framework, environment types, and agent architectures.

Unit 3

Search Techniques for Problem Solving — Formulating problems as state-space search, uninformed strategies (BFS, DFS, UCS), and informed heuristic search (greedy best-first, A*).

Unit 4

Optimization and Constraint Satisfaction — Local search, simulated annealing, genetic algorithms, and constraint satisfaction problems (CSPs) with backtracking and heuristics.

Unit 5

Introduction to Logic — Propositional logic, logical equivalences, validity, and why formal reasoning matters for AI knowledge representation.

Unit 6

Knowledge-Based Agents and Inference — Building knowledge bases, inference rules, forward and backward chaining, Horn clauses, and expert systems.

Unit 7

Probability and Uncertainty — Probability fundamentals, conditional probability, Bayes' theorem, Bayesian networks, and reasoning under uncertainty.

Unit 8

Machine Learning Foundations — What is machine learning? Supervised learning, model evaluation, bias-variance tradeoff, and the current AI landscape.

Supplementary Reading

These pages provide additional depth on topics that connect multiple units:

Decision Theory and Expected Utility — How rational agents choose actions by combining probabilities with preferences.
K-Nearest Neighbors (k-NN) — A simple but powerful classification algorithm that learns by example.

Open Educational Resource

This textbook is an open educational resource (OER) built on openly licensed materials. Primary content is adapted from the UC Berkeley CS 188 Online Textbook by Nikhil Sharma, Josh Hug, Jacky Liang, and Henry Zhu, licensed under CC BY-SA 4.0. Additional sources include forall x: Calgary (CC BY 4.0), OpenStax Introductory Statistics (CC BY 4.0), and U.S. government public domain materials from NIST and the EU AI Act.

This work is licensed under CC BY-SA 4.0.