Introduction to Artificial Intelligence: CSCI 3202

Course offered Fall 2006.

Location:	Tuesday and Thursday 12:30pm-1:45pm in ECCR 1B51
Instructor:	Professor Greg Grudic
Office:	ECOT 525
Office Hours:	Tuesday and Thursday 2:00 to 3:00 And By Appointment
Phone:	303-492-4419
Email:	grudic@cs.colorado.edu
Course URL:	http://www.cs.colorado.edu/~grudic/teaching/CSCI3202_2006

Course Syllabus

Homeworks:

· Homework 1: Equations from Code (Assigned: September 7, 2006. Due: September 21, 2006). Matlab Code (zipped). This assignment is worth 5% of your final mark.

· Homework 2: Support Vector Machine Classification. (Assigned: Sept. 28, 2006. Due: Oct. 17 2006). Data sets X_2_trn.txt, Y_2_trn.txt, X_2_val.txt, Y_2_val.txt, X_2_tst.txt. This assignment is worth 20% of your final mark.

· Homework 3: Density Estimation and Classification. (Assigned: October 26, 2006. Due November 30, 2006). You must use the code template in Template_Code.zip. This assignment is worth 40% of your final mark. Here is the data (these are matlab files that have the same format as those data files used by the template code):

1. Learning Data: HW_3_Lrn_data.mat

2. Test data with labels: HW_3_Tst_1_data.mat (report your error on the test data)

3. Test data without labels: HW_3_Tst_2_data.mat (you need to hand in your predictions for every test point)

Grading:

Homework Assignments (4) 70%

Final Exam 30%

Lectures:

1. August 28, 2006 (Tuesday): Introduction

2. August 31, 2006 (Thursday): Introduction

3. September 5, 2006 (Tuesday): Introduction to Classification – Perceptron Algorithm

4. September 7, 2006 (Thursday): Nearest Neighbor Classification and Regression. Model Selection and Estimating Model Error. Homework 1 Assigned – Due on September 21.

1. September 12, 2006 (Tuesday): Model Selection and Estimating Model Error

2. September 14, 2006 (Thursday): Support Vector Machine Classification.

3. September 19, 2006 (Tuesday): Support Vector Machine Classification and Regression.

4. September 21, 2006 (Thursday): Support Vector Machine Classification and Regression.

5. September 26, 2006 (Tuesday): Neural Networks.

6. September 28, 2006 (Thursday): Neural Networks.

7. October 3, 2006 (Tuesday): Classification and Regression Trees

8. October 5, 2006 (Thursday): Ensemble learning (Boosting. Bagging, Random Forests).

9. October 10, 2006 (Tuesday): Search 1

10.October 12, 2006 (Thursday): Planning 1

11.October 17, 2006 (Tuesday): Planning 2

12.October 19, 2006 (Thursday): Optimal Decision Theory

13.October 24, 2006 (Tuesday): Density Estimation and Single Class Learning. And a TAKE HOME QUIZZ ON SLIDE 16

14.October 26, 2006 (Thursday): Computer Vision 1

15.October 31, 2006 (Tuesday): Computer Vision 2

16.November 2, 2006 (Thursday): Bayesian Learning 1

17.November 7, 2006 (Tuesday): Bayesian Learning 2

18.November 9, 2006 (Thursday): Bayesian Learning 3

19.November 14, 2006 (Tuesday): Reinforcement Learning

20.November 16, 2006 (Thursday): Clustering and Dimensionality Reduction

21.November 21, 2006 (Tuesday): No Class

22.November 23, 2006 (Thursday): No Class

23.November 28, 2006 (Tuesday): Sparse Models 1

24.November 30, 2006 (Thursday): Sparse Models 2

25.December 5, 2006 (Tuesday): Historical Prospective 1

26.December 7, 2006 (Thursday): Historical Prospective 2

27.December 12, 2006 (Tuesday): Review 1

28.December 14, 2006 (Thursday): Review 2