COL774: Machine Learning

Course Content

Class Notes (Date-Wise)

jan18.pdf, jan19.pdf, jan23.pdf, jan25.pdf, jan30.pdf, feb1.pdf, feb9.pdf, feb13.pdf, feb16.pdf, feb19.pdf, feb20.pdf, feb22.pdf, feb23.pdf, mar6.pdf, mar8.pdf, mar9.pdf, mar15.pdf, mar16.pdf, mar20.pdf mar22_23.pdf apr3.pdf apr5.pdf apr10.pdf apr12.pdf apr13.pdf apr17.pdf apr19.pdf apr20.pdf apr24.pdf apr26.pdf apr27.pdf apr28.pdf

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, Machine Learning. Tom Mitchell.
• Deep Learning Book
  

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 assignment problems with other students in the class. But all your code should be produced independently without looking at/referring to anyone else's code.
2. Python and MATLAB are the default programming languages for the course. You should use one of these two languages for programming your assignments unless otherwise explicitly allowed.
3. Code should be submitted using Moodle Page. Make sure to include commenrs for readability.
4. 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.
5. Honor Code: Any cases of copying will be awarded a zero on the assignment and a penalty of -10. More severe penalties may follow.
6. 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.

• Minor 1 from the last Offering of the Course
• Pratice Questions for Minor 1
• Pratice Questions for Minor 2
• Pratice Questions for Major (You should also revise earlier released practice questions).

1. Assignment 3. Due Date: Sunday April 8, 2018. 11:50 pm.
   Datasets/Scripts:
   • Question 1: Dataset: dtree_data.zip [Updated: 9:42 am, Mar 15]
     Attribute Description: attr.csv. Scripts (for reading): read_data.py, read_data.mat
   • Question 2: Toy: toy_data.zip (data), visualization.py (script). MNIST: mnist_data.zip (data).
2. Assignment 2
   Datasets: Check out the links in the Assigment text.
   Due Date: Sunday Mar 11, 2018. 11:50 pm.
3. Assignment 1
   Datasets: ass1_data.zip
   Due Date: Monday February 12, 2018. 11:50 pm.

Grading Policy (Tentative)