Difference between revisions of "CS3600 Summer 2017"
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==2017 Summer Schedule== | ==2017 Summer Schedule== | ||
| − | {|! Week !! Topic !! Due | + | This schedule is tentative and subject to change due to the compressed summer timeline. I am not certain exactly how quickly we will progress through the material. |
| − | | | + | |
| − | | | + | The '''midterm''' will be given in week 6 or 7 (after Bayes Nets) depending on our progress. I will nail it down early in the class. |
| − | |- 2 || Problem Solving, Search || Project 0 | + | |
| − | |- 3 || Informed Search, Constraint Satisfaction Problems | + | The '''final''' will be given according to the Registrar's schedule for final exams. |
| − | |- 4 || CSP, Logic || Project 1 | + | |
| − | |- 5 || Reasoning with Uncertainty, Probability | + | {| class="wikitable" |
| − | |- 6 || Bayes Nets, Dynamic Bayes Nets || Project 2 | + | ! Week !! Topic !! Due |
| − | |- 7 || Intro Machine Learning, Decision Trees | + | |- |
| − | |- 8 || Local Search, Optimization || Project 3 | + | |1 || Intro to AI, Agents, Environments || |
| − | |- 9 || Neural Networks | + | |- |
| − | |- 10 || Decision Making, Markov Decision Processes || Project 4 | + | |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 | ||
|} | |} | ||
Revision as of 18:45, 10 May 2017
Contents
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
This schedule is tentative and subject to change due to the compressed summer timeline. I am not certain exactly how quickly we will progress through the material.
The midterm will be given in week 6 or 7 (after Bayes Nets) depending on our progress. I will nail it down early in the class.
The final will be given according to the Registrar's schedule for final exams.
| 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 |
