CSE 415 Spring 2017
Course Calendar

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March
MondayTuesdayWednesdayThursdayFriday
27
14:30-15:20 Lecture
SAV 264
Introduction
Slides
28 29
14:30-15:20 Lecture
SAV 264
Python for AI
Slides
30 31
14:30-15:20 Lecture
SAV 264
Implementing Conversational Agents
Slides

April
MondayTuesdayWednesdayThursdayFriday
03
14:30-15:20 Lecture
SAV 264
More Python Techniques
Slides
04 05
14:30-15:20 Lecture
SAV 264
Knowledge Representation with Inheritance Hierarchies
Slides
23:59 Assignment 1 (Programming Techniques and Conversational Agents) due
06 07
14:30-15:20 Lecture
SAV 264
Problem Representation for State-Space Search
Slides
10
14:30-15:20 Lecture
SAV 264
Search Algorithms and Heuristics
Slides
11 12
14:30-15:20 Lecture
SAV 264
Search Algorithms (cont.)
23:59 Assignment 2 (Knowledge Management) due
13 14
14:30-15:20 Lecture
SAV 264
Problem Formulation.
Slides
17
14:30-15:20 Lecture
SAV 264
Advanced Search Algorithms.
Slides
18 19
14:30-15:20 Lecture
SAV 264
Advanced Search Techniques (continuing the slides from April 17).
23:59 Assignment 3 (Problem Solving) due
20 21
14:30-15:20 Lecture
SAV 264
Minimaxing
Slides
24
14:30-15:20 Midterm exam
25 26
14:30-15:20 Lecture
SAV 264
Game-playing (continued)
27 28
14:30-15:20 Lecture
SAV 264
About Assignment 5: Rules of Baroque Chess and K-in-a-Row.
23:59 Assignment 4 (Problem Formulation) due

May
MondayTuesdayWednesdayThursdayFriday
01
14:30-15:20 Lecture
SAV 264
Game-playing: Alpha-Beta Pruning. (Also: Optional quiz on A* Search and Heuristics).
Slides
02 03
14:30-15:20 Lecture
SAV 264
Expectimax Search
Slides
04 05
14:30-15:20 Lecture
SAV 264
Propositional Calculus and Resolution
Slides
08
14:30-15:20 Lecture
SAV 264
Probabilistic Inference with the Rule of Thomas Bayes
Slides
23:59 Assignment 5 (Game Agents) due
09 10
14:30-15:20 Lecture
SAV 264
Markov Decision Processes
Slides
11 12
14:30-15:20 Lecture
SAV 264
MDPs (continued)
15
14:30-15:20 Lecture
SAV 264
Agent Competition Finals; MDP Value Iteration
16 17
14:30-15:20 Lecture
SAV 264
Reinforcement Learning
Slides
23:59 Assignment 6 (Part 1) (Bayes Rule and Markov Decision Processes) due
18 19
14:30-15:20 Lecture
SAV 264
Reinforcement Learning (continued)
22
14:30-15:20 Lecture
SAV 264
The Joint Distribution and Conditional Independence
Slides
23:59 Assignment 6 (Part 2) (Reinforcement Learning) due
23
23:59 Project Planning Questionnaire due
24
14:30-15:20 Lecture
SAV 264
Naive Bayes Classification
Slides
25 26
14:30-15:20 Lecture
SAV 264
Introduction to Perceptrons and Neural Network Learning
Slides
29
Memorial Day
30
23:59 Project code due
31
14:30-15:20 Lecture
SAV 264
Project Presentations
01 02
14:30-15:20 Lecture
SAV 264
The Future of AI
Slides
23:59 Project report due

June
MondayTuesdayWednesdayThursdayFriday
05 06
14:30-16:20 Final exam
07 08 09