CS3600 Summer 2018 - Revision history

Dave: /* 2018 Summer Schedule */

2018-06-13T17:21:39Z

2018 Summer Schedule

Dave: /* 2018 Summer Schedule */

2018-06-13T17:01:47Z

2018 Summer Schedule

Dave: /* 2018 Summer Schedule */

2018-06-13T17:01:24Z

2018 Summer Schedule

Dave: /* 2018 Summer Schedule */

2018-06-13T17:01:07Z

2018 Summer Schedule

Dave: /* 2018 Summer Schedule */

2018-05-14T20:49:45Z

2018 Summer Schedule

Dave: /* 2017 Summer Schedule */

2018-05-14T19:33:03Z

2017 Summer Schedule

Dave: /* Projects (50%) */

2018-05-14T19:20:24Z

Projects (50%)

Dave: /* Office Hours */

2018-05-14T19:19:52Z

Office Hours

Dave: /* Overview */

2018-05-14T19:18:58Z

Overview

Dave: Created page with "==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 in..."

2018-05-14T19:18:49Z

Created page with "==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 in..."

New page

==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.

==Office Hours==

Dave: <B>TTH 4:25-5:30, Klaus 1447</B>. 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!

==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.

{| class="wikitable"
! Week !! Topic !! Due
|-
|1 || Intro to AI, Agents, Environments ||
|-
|2 || Problem Solving, Search || Project 0
|-
|3 || Informed Search, Constraint Satisfaction Problems || Project 1
|-
|4 || CSP, Logic ||
|-
|5 || Reasoning with Uncertainty, 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 ||
|}

@@ Line 48: / Line 48: @@
 |5 || Particle Filtering, Intro Machine Learning || Bayes Net Project (extended to following Friday)
 |-
-|6 || Decision Trees, Midterm ||
+|6 || Decision Trees, Decision Making ||
 |-
-|7 || Decision Trees, Decision Making, Markov Decision Processes || Midterm (Tuesday)
+|7 || Midterm, Markov Decision Processes || Midterm (Tuesday)
 |-
 |8 || Markov Decision Processes, Reinforcement Learning || Decision Trees Project

@@ Line 46: / Line 46: @@
 |4 || Bayes Nets, Dynamic Bayes Nets ||
 |-
-|5 || Particle Filtering, Intro Machine Learning || Bayes Net Project *extended to following Friday
+|5 || Particle Filtering, Intro Machine Learning || Bayes Net Project (extended to following Friday)
 |-
 |6 || Decision Trees, Midterm ||
 |-
-|7 || Decision Trees, Decision Making, Markov Decision Processes || Midterm
+|7 || Decision Trees, Decision Making, Markov Decision Processes || Midterm (Tuesday)
 |-
 |8 || Markov Decision Processes, Reinforcement Learning || Decision Trees Project

@@ Line 46: / Line 46: @@
 |4 || Bayes Nets, Dynamic Bayes Nets ||
 |-
-|5 || Particle Filtering, Intro Machine Learning || Bayes Net Project ```(extended to following Friday)```
+|5 || Particle Filtering, Intro Machine Learning || Bayes Net Project *extended to following Friday
 |-
 |6 || Decision Trees, Midterm ||

@@ Line 31: / Line 31: @@
 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 '''midterm''' will be given on '''Tuesday, June 26'''.  Scores will be returned before drop day.
 The '''final''' will be given according to the Registrar's schedule for final exams.
@@ Line 46: / Line 46: @@
 |4 || Bayes Nets, Dynamic Bayes Nets ||
 |-
-|5 || Particle Filtering, Intro Machine Learning || Bayes Net Project
+|5 || Particle Filtering, Intro Machine Learning || Bayes Net Project ```(extended to following Friday)```
 |-
-|6 || Decision Trees, Midterm || Midterm
+|6 || Decision Trees, Midterm ||
 |-
-|7 || Decision Trees, Decision Making, Markov Decision Processes ||
+|7 || Decision Trees, Decision Making, Markov Decision Processes || Midterm
 |-
 |8 || Markov Decision Processes, Reinforcement Learning || Decision Trees Project

@@ Line 40: / Line 40: @@
 |1 || Intro to AI, Agents, Environments, Problem Solving, Search ||
 |-
-|2 || Search, Informed Search || Project 0
+|2 || Search, Informed Search || Intro Project (optional)
 |-
-|3 || Informed Search, Local Search || Project 1
+|3 || Informed Search, Probability || Search Project
 |-
-|4 || Logic, Reasoning with Uncertainty ||
+|4 || Bayes Nets, Dynamic Bayes Nets ||
 |-
-|5 || Probability ||Project 2
+|5 || Particle Filtering, Intro Machine Learning || Bayes Net Project
 |-
-|6 || Bayes Nets, Dynamic Bayes Nets ||  Midterm
+|6 || Decision Trees, Midterm || Midterm
 |-
-|7 || Intro Machine Learning, Decision Trees ||Project 3
+|7 || Decision Trees, Decision Making, Markov Decision Processes ||
 |-
-|8 || Local Search, Optimization ||
+|8 || Markov Decision Processes, Reinforcement Learning || Decision Trees Project
 |-
-|9 || Neural Networks || Project 4
+|9 || Local Search, Optimization ||
 |-
-|10 || Decision Making, Markov Decision Processes ||
+|10 || Neural Networks || MDP Project
 |-
 |11 || Review for Final ||
 |}

@@ Line 27: / Line 27: @@
 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==
+==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 (after Bayes Nets) depending on our progress.  I will nail it down early in the class.
+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.
@@ Line 38: / Line 38: @@
 ! Week !! Topic !! Due
 |-
-|1 || Intro to AI, Agents, Environments ||
+|1 || Intro to AI, Agents, Environments, Problem Solving, Search ||
 |-
-|2 || Problem Solving, Search || Project 0
+|2 || Search, Informed Search || Project 0
 |-
-|3 || Informed Search, Constraint Satisfaction Problems || Project 1
+|3 || Informed Search, Local Search || Project 1
 |-
-|4 || CSP, Logic ||
+|4 || Logic, Reasoning with Uncertainty ||
 |-
-|5 || Reasoning with Uncertainty, Probability ||Project 2
+|5 || Probability ||Project 2
 |-
 |6 || Bayes Nets, Dynamic Bayes Nets ||  Midterm

@@ Line 13: / Line 13: @@
 ===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).
+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%)===

@@ Line 5: / Line 5: @@
 ==Office Hours==
-Dave: <B>TTH 4:25-5:30, Klaus 1447</B>.  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!
+Dave: <B>TTH 4:25-5:30, Klaus 2443</B>.  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.)
 ==Assignments and Grading==

@@ Line 1: / Line 1: @@
 ==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.
+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==