Introduction to Artificial Intelligence - Honors (CS 370)

Course Information

Course Description

This course introduces the theoretical and computational techniques that serve as a foundation for the study of artificial intelligence (AI). Topics include:

• Introduction to AI and background: What is AI? Related fields.
• Problem solving by search: Principles of search; uninformed (“blind”) search; informed (“heuristic”) search; local search; genetic algorithms; adversarial search.
• Knowledge representation and reasoning: Knowledge bases; propositional logic; satisfiability; constraint satisfaction problems (CSPs); planning; logical and probabilistic inference.
• Game theory: Games with hidden information; non-zero-sum games.

All classes are held in person. Assignments are submitted through Canvas, and course discussion takes place on Harmonize.

Learning Outcomes

By the end of this course, students will be able to:

• Model and solve search problems using uninformed and heuristic algorithms, and evaluate each algorithm’s time/space complexity and optimality on a given problem formulation.
• Apply and evaluate local search algorithms to optimize objective functions, reporting solution quality and convergence behavior.
• Formulate real-world tasks as CSPs and solve CSP instances using backtracking, MRV/LCV heuristics, and arc consistency.
• Encode knowledge in propositional logic and SAT and derive conclusions by resolution; when appropriate, translate problems to SAT and validate answers with solver outputs.
• Build Bayesian networks and perform exact and approximate inference.
• Model sequential processes with HMMs and interpret results in the context of uncertainty over time.
• Design adversarial agents for perfect-information games using minimax and alpha-beta pruning and analyze branching-factor impacts on search performance.
• Reason about games with hidden information (zero-sum and non-zero-sum).
• Apply foundational machine-learning methods (supervised learning, regression/classification, and clustering) to real-world datasets, selecting metrics and reporting performance with appropriate caveats about assumptions and data.

Prerequisites

CS 114, MATH 337 (or MATH/EssexCC 239), and CS 241 with a grade of C or better.

Course Materials

• Textbook: Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. 4th US ed. (R&N).
• Selected papers and handouts.

Grading

Small class-wide adjustments may be applied at the end of the semester if an assessment proves unusually challenging or easy; any adjustment applies consistently to all students. Class participation, attendance, and periodic pop quizzes may be considered to bump a final letter grade near a boundary.

Tentative Course Content

R&N refers to the required textbook by Stuart Russell and Peter Norvig.

• Introduction [R&N, Chapter 1]
• Intelligent agents [R&N, Chapter 2]
• Search [R&N, Chapter 3]
• Advanced search schemes [R&N, Chapter 4]
• Adversarial search [R&N, Chapter 5]
• Constraint satisfaction [R&N, Chapter 6]
• Propositional logic [R&N, Chapter 7]
• Exam 1
• Probability theory review [R&N, Chapter 13]
• Probabilistic reasoning [R&N, Chapter 14]
• Probabilistic reasoning over time [R&N, Chapter 15]
• Machine learning [R&N, Chapter 18]
• Advanced topics [TBD]
• Exam 2

Course Policies

• Makeup exams: Considered only for documented circumstances approved by the Dean of Students.
• Exams: Both exams are taken on the scheduled dates; a one-page crib sheet is allowed.
• Late work: 10% penalty per day; work more than seven days late is not graded. Extensions require documentation verified by the Dean of Students.
• Attendance and participation: Attendance is mandatory; arriving late or leaving early counts as half an absence. Please keep devices away unless they are used for a class activity.
• Regrades: Request within one week of the work being returned.
• AI tools: Allowed as a supplementary learning resource only; AI tools may not be used to generate, refine, or complete any graded work.
• Collaboration: Discuss concepts and high-level approaches freely, but write and implement all submitted work independently. Do not share, view, copy, or discuss Python code; list discussion partners on your submission.
• Communication: Post course questions on Harmonize; for email, begin the subject with “CS 370.H01:”. Expect replies within two business days.

The full syllabus includes the complete course and university policies.

Additional Information