COL774: Machine Learning

Course Content

Additional Resources

• Notes on Avoiding Overfitting in Decision Trees
• On Convergence of Stochastic Gradient Descent (Youtube Video by Andrew Ng)
• On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. Andrew Y. Ng and Michael Jordan. NIPS 2001.
• Generative and Discrminative Classifiers: Naive Bayes and Logistic Regression. Chapter 1, Maching Learning. Tom Mitchell.

Review Material

Topic	Notes
Probability	prob.pdf
Linear Algebra	linalg.pdf
Gaussian Distribution	gaussians.pdf
Convex Optimization (1)	convex-1.pdf

References

1. Machine Learning: A Probabilistic Perspective. Kevin Murphy. MIT Press, 2012.
2. Pattern Recognition and Machine Learning. Christopher Bishop. First Edition, Springer, 2006.
3. Pattern Classification. Richard Duda, Peter Hart and David Stock. Second Edition, Wiley-Interscience, 2000.
4. Machine Learning. Tom Mitchell. First Edition, McGraw-Hill, 1997.

1. You are free to discuss the problems with other students in the class. You should include the names of the people you had a significant discussion with in your submission.
2. All your solutions should be produced independently without referring to any discussion notes or the code someone else would have written.
3. All the programming should be done in MATLAB (unless otherwise allowed explicitly). Include comments for readability.
4. Code should be submitted using Moodle Page.
5. Create a separate directory for each of the questions named by the question number. For instance, for question 1, all your submissions files should be put in the directory named Q1 (and so on for other questions). Put all the Question sub-directories in a single top level directory. This directory should be named as "yourentrynumber_firstname_lastname". For example, if your entry number is "2013anz7535" and your name is "Nilesh Pathak", your submission directory should be named as "2013anz7535_nilesh_pathak". You should zip your directory and name the resulting file as "yourentrynumber_firstname_lastname.zip" e.g. in the above example it will be "2013anz7535_nilesh_pathak.zip". This single zip file should be submitted online.
6. Honor Code: Any cases of copying will be awarded a zero on the assignment and a penalty of -10. More severe penalties may follow.
7. Updated: Jan 23, 2016. Late Policy: You are allowed a total of 5 late days acorss all the assignments. You are free to decide how you would like to use them. You will get a zero on an assignment once you exceed the (total) allowed exemption of 5 days.

• Pratice Questions for Major
• Pratice Questions for Minor 2

1. Assignment 4. Due: Sunday May 1, 2016. 11:50 pm.
   Datasets:
   • Question 1: kmeans_data.zip
   • Question 2: em_data.zip
   • Question 4: pca_data.zip (Not Graded!)
2. Assignment 3. Due: Sunday April 10, 2016. 11:50 pm.
   Datasets:
   • Question 1: dtree_data_code.zip
   • Question 2: nb_data.zip
3. Assignment 2. Due: Wednesday Mar 16, 2016. 11:50 pm.
   Datasets:
   • Question 1: train.data, test.data. Files Updated: Sunday Feb 28, 5:10 pm
4. Assignment 1. Due: Sunday February 7, 2016. 11:50 pm.
   Datasets:
   • Question 1: q1x.dat, q1y.dat
   • Question 2: q2x.dat, q2y.dat
   • Question 3: q3x.dat, q3y.dat
   • Question 4: q4x.dat, q4y.dat

Grading Policy (Tentative)