Assignments

CSE 415: Introduction to Artificial Intelligence The University of Washington, Seattle, Autumn 2026

All deadlines are at 11:59 PM on the dates shown. See the Policies page for grace days and grading weights. Each programming assignment other than A0 includes a Report component; see the Assignment Reports page. Programming assignments are turned in via Gradescope; the turn-in method for the written exercises (A4 and A6) is to be announced.   Assignment 0: Python Warmup. Due Monday, October 5. This is a participation item (like a worksheet), worth 1 point of participation credit. Lead TA: Eva Jain; co-lead: Sanghun Jung.   Assignment 1: Problem Formulation and Basic Search. Due Monday, October 12. You may work solo on this or in a partnership of two. Lead TA: Eva Jain; co-lead: Song Kim.   Assignment 2: Heuristic Search. Due Monday, October 19. You may work solo on this or in a partnership of two. Lead TA: Deniz Nazar; co-lead: Song Kim.   Assignment 3: Adversarial Search in the game of "K in a Row with Forbidden Squares." Due Friday, October 30. You may do this assignment either individually or in a team of 2 (preferred). This assignment counts for 12 percent of the course grade, and tournament results will be presented in class on Wednesday, December 9. Lead TA: Emilia Gan; co-lead: Eva Jain.   Assignment 4: Written Exercises I (individual work; questions generated in PrairieLearn). Due Friday, November 6. These exercises cover state-space search, heuristics, adversarial search, expectimax, and Markov Decision Processes, including value iteration. Solutions and/or solution hints will be released before the midterm exam. Lead TA: Sanghun Jung; co-lead: Deniz Nazar.   Assignment 5: Reinforcement Learning: Value Iteration and Q-Learning. Due Monday, November 23. Partnership is optional. Lead TA: Deniz Nazar; co-lead Song Kim.   Assignment 6: Written Exercises II (individual work; questions generated in PrairieLearn). Due Monday, November 30. These exercises cover material from lectures since Written Exercises I, through about November 23: Q-learning, joint probability distributions, Bayes nets and d-separation, Markov models, hidden Markov models and the Viterbi algorithm, and ethical issues in AI. Lead TA: Emilia Gan; co-lead Eva Jain   Assignment 7: Deep Learning: perceptron training, multilayer networks, and backpropagation. Due Monday, December 7. Lead TA: Song Kim; co-lead Sanghun Jung.