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Assignments

Course exercises follow the exercises/chNN/ sources from the aima book repository, rendered as pages in this site (see Textbook chapter exercises). Assignments are distributed via GitHub Classroom starter repositories.

Schedule: the list below is 24 class sessions in 8 weeks of 3 meetings each (see Syllabus and course/ains5001-8week-schedule.json); each session links the AIMA slide deck (lecture-NN) and matching chapter exercises.

Exercises by class week (8 weeks, 3 meetings per week, 24 class sessions)

Format: 8 weeks × 3 class meetings = 24 sessions on the Artificial Intelligence: A Modern Approach (4e) slide decks. Each link targets the site file lecture-NN (AIMA deck / book chapter). A full chapter index lists every book chapter. Exercise numbers (C.N) follow the book; GitHub Classroom usually assigns a subset — follow the instructor’s rubric.

Lecture 0: GitHub Classroom Assignments

Week 1 — Foundations

Class session 1/24: Introduction (AIMA deck lecture-01)

Class session 2/24: Intelligent Agents (AIMA deck lecture-02)

Class session 3/24: Solving Problems by Searching (AIMA deck lecture-03)

Week 2 — Search and games

Class session 4/24: Search in Complex Environments (AIMA deck lecture-04)

Class session 5/24: Adversarial Search and Games (AIMA deck lecture-05)

Class session 6/24: Constraint Satisfaction Problems (AIMA deck lecture-06)

Week 3 — Logic and knowledge

Class session 7/24: Logical Agents (AIMA deck lecture-07)

Class session 8/24: First-Order Logic (AIMA deck lecture-08)

Class session 9/24: Inference in First-Order Logic (AIMA deck lecture-09)

Week 4 — Knowledge, planning, and uncertainty

Class session 10/24: Knowledge Representation (AIMA deck lecture-10)

Class session 11/24: Quantifying Uncertainty (AIMA deck lecture-12)

Class session 12/24: Automated Planning (AIMA deck lecture-11)

Week 5 — Probabilistic reasoning

Class session 13/24: Probabilistic Reasoning (AIMA deck lecture-13)

Class session 14/24: Probabilistic Reasoning over Time (AIMA deck lecture-14)

Class session 15/24: Probabilistic Programming (AIMA deck lecture-15)

Week 6 — Decision making

Class session 16/24: Making Simple Decisions (AIMA deck lecture-16)

Class session 17/24: Making Complex Decisions (AIMA deck lecture-17)

Class session 18/24: Multiagent Decision Making (AIMA deck lecture-18)

Week 7 — Machine learning

Class session 19/24: Learning from Examples (AIMA deck lecture-19)

Class session 20/24: Learning Probabilistic Models (AIMA deck lecture-20)

Class session 21/24: Deep Learning (AIMA deck lecture-21)

Week 8 — Applications and conclusions

Class session 22/24: Reinforcement Learning (AIMA deck lecture-22)

Class session 23/24: Natural Language Processing (AIMA deck lecture-23)

Class session 24/24: Philosophy, Ethics, and Safety of AI (AIMA deck lecture-27)

Lecture slides
Worked examples
Textbook chapter exercises
Textbook exercises by chapter