Difference between revisions of "CS3600 Summer 2018"
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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. | 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. | ||
| − | == | + | ==2018 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. | 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 | + | The '''midterm''' will be given in week 6 or 7 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. | The '''final''' will be given according to the Registrar's schedule for final exams. | ||
| Line 38: | Line 38: | ||
! Week !! Topic !! Due | ! Week !! Topic !! Due | ||
|- | |- | ||
| − | |1 || Intro to AI, Agents, Environments || | + | |1 || Intro to AI, Agents, Environments, Problem Solving, Search || |
|- | |- | ||
| − | |2 || | + | |2 || Search, Informed Search || Project 0 |
|- | |- | ||
| − | |3 || Informed Search, | + | |3 || Informed Search, Local Search || Project 1 |
|- | |- | ||
| − | |4 || | + | |4 || Logic, Reasoning with Uncertainty || |
|- | |- | ||
| − | |5 || | + | |5 || Probability ||Project 2 |
|- | |- | ||
|6 || Bayes Nets, Dynamic Bayes Nets || Midterm | |6 || Bayes Nets, Dynamic Bayes Nets || Midterm | ||
Revision as of 14:33, 14 May 2018
Contents
Overview
You are on the page for information specific to the Summer 2018 session of this course. Go here Undergrad_Intro_AI_Course for overall course policies and information.
Office Hours
Dave: TTH 4:25-5:30, Klaus 2443. There is no class after us in our classroom, so I will simply stay there for up to an hour after every class, for as long as someone wants to talk to me! (If there does end up being a class after us, we will find another location.)
TA office hours: TBA.
Assignments and Grading
Projects (50%)
There will be four equally-weighted projects in the class, covering search algorithms, Markov decision processes, 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.
2018 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 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, Problem Solving, Search | |
| 2 | Search, Informed Search | Project 0 |
| 3 | Informed Search, Local Search | Project 1 |
| 4 | Logic, Reasoning with Uncertainty | |
| 5 | Probability | Project 2 |
| 6 | Bayes Nets, Dynamic Bayes Nets | Midterm |
| 7 | Intro Machine Learning, Decision Trees | Project 3 |
| 8 | Local Search, Optimization | |
| 9 | Neural Networks | Project 4 |
| 10 | Decision Making, Markov Decision Processes | |
| 11 | Review for Final |
