Course Instructor: Greg Grudic
|
Class
Location: |
TR
12:30-1:45 ECCR
105 |
|
My
Office: |
ECOT
525 |
|
Office
Hours: |
Wednesdays10:00
to 11:00. Thursdays 2:00 to 3:00. And
By Appointment. |
|
Phone: |
303-492-4419 |
|
Email: |
grudic@cs.colorado.edu |
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Course
URL: |
http://www.cs.colorado.edu/~grudic/teaching/CSCI3202_2007 |
This course will focus on developing a practical understanding of the fundamentals of Artificial Intelligence. The main focus of the course will be a group project, which will involve putting together a complete Artificial Intelligent Robotic System. The students will work in groups of 4 or 5 and will implement the complete sensing, computation and action capabilities required for a state of the art Robotic system. The task of the robotic system will be to explore the environment using computer vision, and to build maps that allow it to get from any initial to final position in any environment. Therefore, Sensing will be implemented using Computer Vision, Computation will be implemented using Reasoning, Planning, Machine Learning, and Optimal Decision Theory, and Actuation will be implemented by taking the results of the Computations done and controlling the wheels of the robot in order to execute them.
There will be a robot competition between groups in the final two weeks of the course.
A video of the robot platform which will be used by the groups can be seen at http://ia.cs.colorado.edu/projects/lagr/videos/autonomous_demo.mov
Grading:
Group Project 60%
Homework Assignments 30%
Class Participation 10%
(There will be no final
exam)
Mark
Breakdown:
A : ≥
90%
A- : ≥ 85%, <
90%
B+ : ≥ 80%, <
85%
B : ≥ 75%, <
80%
B- : ≥ 70%, <
75%
C+ : ≥ 65%, <
70%
C : ≥ 60%, <
65%
C- : ≥ 55%, <
60%
D : ≥ 50%, <
55%
F : < 50%
Homework:
1.
Homework 1: Due Thursday
September 20 (11:59PM). (HW1.zip).
Worth 3% of final your mark.
2.
Homework 2: Due Thursday
October 2 (11:50PM). (HW2.zip).
Worth 9% of final your mark.
3.
Homework 3: Due Thursday
December 6 (11:55PM). (HW3_Ver_2_Code_Template.zip).
Worth 13% of final your mark.
4.
Homework 4: Due Friday
December 14 (11:55PM). Hand in answers to all quizzes. Worth 5% of final your
mark.
Project
info:
1.
Test 1: Assigned Thursday
October 4, 2007. Code due Tuesday October 23, 2007. Test week is October 24 to
26, 2007. (Project_Part_1.ppt)
(Project_Part_1.pdf).
(tst1_code.zip).
(Project_Test1.pdf).
2.
Test 2: Real time
traversable parts of image labeling. Image Feature Selection (Project_Test2.pdf).
(code)
Assigned Thursday November 8, 2007. Code due 9:00 AM, Monday, December 3. Test
week is December 3 to 7, 2007.
3.
Test 3: Planning robot
paths. Assigned Thursday November 15, 2007. Code due 9:00 AM, Monday December
10, 2007. Test week is December 10 to 14, 2007. (astar_test_files.zip)
4.
Interview with Instructor:
In class during week of December 10 to 14, 2007.
Quizzes:
1.
Quiz 2: (quiz2.pdf)
2.
Quiz 3: (quiz3.pdf)
Lectures:
Intro to AI (IntroAI.ppt).
Classification (Classification.ppt)
Nearest Neighbor (NearestNeighbor.pdf).
Decision Tress.
(Trees.ppt)
Perceptron Algorithm (Perceptron.pdf).
Support Vector Classification (SMV.ppt).
Model Selection and Future Error Prediction (Model_Selection_Error_Prediction.pdf)
Ensemble Learning. (Ensemble_Learning.ppt)
Computer
Vision 1. (VisOverView.pdf)
Computer
Vision 2. (MultiView3D.pdf)