Machine Learning CSCI 5622

 

Fall 2006.

 

Location:          

Wednesdays 3:00pm-5:30pm ECCR 108

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/CSCI5622_2006

 

 

Course Syllabus

 

 

 

Homeworks:

·       Homework 1: Equations from Code (Assigned: September 6, 2006. Due: September 20, 2006). Matlab Code (zipped). This assignment is worth 3% of your final mark.

·       Homework 2: Matlab Code From Equations (Assigned: September 13, 2006. Due: September 27, 2006). Matlab code: Demo_Gaussian_Kernel.m, Demo_Poly_Kernel.m, and Demo_Sigmoid_Kernel.m. This assignment is worth 3% of your final mark.

·       Homework 3: Kernel Ridge Regression (Assigned: September 20, 2006. Due: October 11, 2006). This assignment is worth 10% of your final mark.

- Data: The data for this homework is in X_lrn_HW3.txt and Y_lrn_HW3.txt. Example Matlab code for reading this data is in Read_HW3_Data.m.

·       Homework 4: Implementing a Learning Algorithm from Equations and Learning to use SVMs. (Assigned: October 11, 2006. Due: November 15, 2006). This assignment is worth 29% of your final mark. Data X_4_trn.txt, Y_4_trn.txt, X_4_val.txt, Y_4_val.txt, and X_4_tst.txt. Hints file.

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Quiz:

·       Take home quiz 1: Regression (Assigned: September 13, 2006. Due: September 20, 2006 at start of class). This quiz is worth 1% of your final mark.

 

 

Weekly Class Schedule:

 

  1. August 30: Introduction. Class Survey Survey.txt (email to me by end of week).
  2. September 6: Introduction to Regression. Ridge Regression Summary. Homework 1 Assigned – Due on September 20
  3. September 13: Introduction to Classification. Assumptions and Definitions. Homework 2 Assigned – Due on September 27
  4. September 20: Perceptron Algorithm. Model Selection and Estimating Model Error. Homework 3 Assigned – Due on October 11
  5. September 27: Support Vector Machine Classification.
  6. October 4: Support Vector Machine Regression.
  7. October 11: Neural Networks. Nearest Neighbor Classification and Regression. Homework 4 Assigned – Due on November 1
  8. October 18: Classification and Regression Trees.
  9. October 25: Ensemble learning (Boosting. Bagging, Random Forests).
  10. November 1: Linear and Quadratic Discriminant Classification Models. Density Estimation with Mixture of Gaussians. Data for class project posted.
  11. November 8: Supervised Learning Summary.
  12. November 15: Midterm
  13. November 22: No Class
  14. November 29: Clustering, Spectral Clustering, Semi-Supervised Learning
  15. December 6: Reinforcement Learning.
  16. December 13: Summary.