View source for CS3600 Summer 2017

==Overview==

You are on the page for information specific to the Summer 2017 session of this course.  Go here [[Undergrad_Intro_AI_Course]] for overall course policies and information.

==Assignments and Grading==

===Projects (50%)===

There will be four equally-weighted projects in the class, covering search algorithms, constraint satisfaction problems, bayesian networks, and machine learning algorithms (decision trees and/or neural networks).

===Exams (50%)===

There is a midterm exam (20%) and a final exam (30%).

===Extra Credit===

The projects have ''significant'' extra credit built in.  If you wish to maximize your grade, this is the place to do it.  Requests for extra credit opportunities or free points at the end of the semester will be referred to this statement.

==Thresholds==

As mentioned on the main page, final course grades are '''not''' rounded up.  While I understand the frustration of having your 89.9% become a B, you should understand that an 89.9% will typically place you in the '''bottom 30%''' of this class.

==2017 Summer Schedule==

{|! Week !! Topic !! Due
|------||------||-------
|- 1 || Intro to AI, Agents, Environments
|- 2 || Problem Solving, Search || Project 0
|- 3 || Informed Search, Constraint Satisfaction Problems
|- 4 || CSP, Logic || Project 1
|- 5 || Reasoning with Uncertainty, Probability
|- 6 || Bayes Nets, Dynamic Bayes Nets || Project 2
|- 7 || Intro Machine Learning, Decision Trees
|- 8 || Local Search, Optimization || Project 3
|- 9 || Neural Networks
|- 10 || Decision Making, Markov Decision Processes || Project 4
|}