CAP 6610, Machine Learning, Fall 2018

Instructor:
Arunava Banerjee
Office: CSE E336.
E-mail: arunava@cise.ufl.edu.
Phone: 505-1556.
Office hours: Wednesday 2:00 p.m.-4:00 p.m.

TA:
XXX XXXX
Office: CSE Exxx.
E-mail: xxx@cise.ufl.edu.
Office hours: Monday x:00 p.m.-x:00 p.m.(at CSE E309) or by appointment.

Pre-requisites:

• The official pre-requisites for this course is COT5615 (Mathematics for Intelligent Systems). Specifically, knowledge of calculus and linear algebra is necessary since we shall be touching on mathematical probability theory. In addition, proficiency in some programming language is a must.

Textbook (recommended): Machine Learning: A Probabilistic Perspective, Murphy, ISBN-10: 0262018020.

Reference: Pattern Recognition and Machine Learning, Bishop, ISBN 0-38-731073-8.

Reference: Pattern Classification, 2nd Edition, Duda, Hart and Stork, John Wiley, ISBN 0-471-05669-3.

Tentative list of Topics to be covered

• Review of mathematical probability theory; finite sample probability bounds.
• Decision Trees
• Bayes decision theory
• Bayesian learning
• Maximum likelihood estimation and Expectation Maximization
• Linear and generalized linear models for regression and classification,
• Sparsity promoting priors with conjugates and their relationship to regularization
• Kernel methods including Support Vector Machines
• Error Back-propagation and Neural Networks
• Mixture models
• Hidden Markov models
• Principal Components Analysis
• Independent Components Analysis
• Reinforcement Learning
• Performance evaluation: re-substitution, cross-validation, bagging, and boosting

The above list is tentative at this juncture and the set of topics we end up covering might change due to class interest and/or time constraints.

Please return to this page at least once a week to check updates in the table below

Evaluation:

• One individual project spanning the semester: 10%
• Homework assignments (written and programming): 30%
• Two midterm exam: 30% each (2 hrs, in-class)
• There will be no makeup exams (Exceptions shall be made for those that present appropriate letters from the Dean of Students Office).

Course Policies:

• Late assignments: All homework assignments are due before class.
• Plagiarism: You are expected to submit your own solutions to the assignments. While the final project and presentation will be done in groups, each member will be required to demonstrate his/her contribution to the work.
• Attendance: Their is no official attendance requirement. If you find better use of the time spent sitting thru lectures, please feel free to devote such to any occupation of your liking. However, keep in mind that it is your responsibility to stay abreast of the material presented in class.
• Cell Phones: Absolutely no phone calls during class. Please turn off the ringer on your cell phone before coming to class.

Academic Dishonesty: See http://www.dso.ufl.edu/judicial/honestybrochure.htm for Academic Honesty Guidelines. All academic dishonesty cases will be handled through the University of Florida Honor Court procedures as documented by the office of Student Services, P202 Peabody Hall. You may contact them at 392-1261 for a "Student Judicial Process: Guide for Students" pamphlet.

Students with Disabilities: Students requesting classroom accommodation must first register with the Dean of Students Office. The Dean of Students Office will provide documentation to the student who must then provide this documentation to the Instructor when requesting accommodation.

Announcements

The second midterm on Dec 5th is NOT cumulative. You will be tested on material covered after Neural networks.

As discussed in class, the final project report (written in the form of a technical conference/journal paper) is due On or BEFORE December 9th midnight.

Midterm 1 will be held on October 12th. Midterm 2 will be held on the last day of classes, December 5th.

HW1 has been posted on Canvas. Please pay speacial attention to deadline and how much space you have for each answer.

I have posted Durrett's book below. You are expected to be comfortable with Chapter 1.

HomeWorks

HomeWork

Due Date

Solutions

List of Topics covered

Week	Topic	Additional Reading
Aug 19 - Aug 25	Putative framework: Supervised, Unsupervised Learning. Reinforcement Learning Labeled/unlabeled datasets, training/testing. Generalization, over-fitting to training data	Dactyl AlphaGo Zero
Aug 26 - Sep 01	Continued with discussing roadmap for the rest of the semester. High level view of topics that we hope to cover why we need to know basic mathematical probability theory	Probability: Theory and Examples by Durrett Edition 4.1. Read and understand Chapter 1.(partiularly sections 1-3) Do exercise problems.
Sep 02 - Sep 08	Sample space, outcome Measurable space, sigma algebra limit supremum and limit infimum probability, random variable distribution function, density
Sep 09 - Sep 15	The "Risk functional" approach Loss function, Hypothesis space Empirical Risk and the Empirical risk minimization principle Application to classification, regression, density estimation Jensen's inequality
Sep 16 - Sep 22	Decision Trees Impurity: Entropy, Gini, Misclasification NP-hardness of the problem Prunning, cross validation
Sep 23 - Sep 29	Multivariate Regression Closed form solution Overdetermined, Underdetermined linear systems Moore-Penrose Pseudo inverse
Sep 30 - Oct 06	Perceptron Learning rule Started mistake bound theorem for perceptron Energy function for perceptron learning and gradient descent	Here Perceptron convergence theorem
Oct 07 - Oct 13	Artificial sigmoidal neuron and gradient descent on error Multi-layer perceptrons and Error back propagation Midterm
Oct 14 - Oct 20	convolution neural networks, recurrent neural networks ReLU, Softmax, Dropout LSTM, ResNet, HighwayNet, DenseNet Background for Support vector machines Constrained optimization problem Convexity, Convex sets, local minima=global minima	LSTM A very good blog post that describes the idea
Oct 21 - Oct 27	Lagrange multipliers Lagrange duality	Lagrange Duality A very good geometric view of what is going on Karush Kuhn-Tucker conditions. Convex Optimization Book by Boyd and Vandenberghe
Oct 28 - Nov 03	Support vector machines maximum margin Primal and dual formulations The kernel trick; polynomial, gaussian kernels
Nov 04 - Nov 10	Unsupervised learning Principal Component Analysis Derivation of Markov, Chebyshev, Hoeffding inequalities
Nov 11 - Nov 17	Overview of Statistical learning theory Vapnik-Chervonenkis dimension Empirical Radamacher Complexity Maximum Likelihood	VC Bound A very good informal description of the theory
Nov 18 - Nov 24	Maximum Likelihood estimates of mean and variance for the multi-variate Normal distribution. Thanksgiving	VC Bound A very good informal description of the theory
Nov 25 - Dec 01	K-Means clustering; objective function and algorithm Mixture of Gaussians and Expectation Maximization.	Wiki on K-Means clustering. Here are D'Souza's notes.