CAP 6610, Machine Learning, Spring 2026

Instructor:
Arunava Banerjee
Office: MALA 6101.
E-mail: arunava@ufl.edu.
Office hours (On Zoom-- 924 861 2325): (E-mail for appointment).

TA:
Tina Salehi Torabi

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.

Reference: Probability: Theory and examples, R. Durrett, can be found online at https://services.math.duke.edu/~rtd/PTE/PTE5_011119.pdf

Reference: 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

• Mathematical Probability theory
• Neural networks including deep learning
• Kernel methods including Support Vector Machines
• Bayes decision theory
• Bayesian learning
• Maximum likelihood estimation and Expectation Maximization
• Mixture models
• Hidden Markov models
• Principal Components Analysis
• Independent Components Analysis
• Monte-Carlo, Markov Chain methods (Gibbs samplers and Metropolis-Hastings)
• 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:

• Homework assignments (written and programming): 25%
• Three midterm exam: 25% each (Time, tbd)
• 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

Midterm dates have been set.
Midterm I on Feb 10th (in class exam)
Midterm II on March 10th (in class exam)
Midterm III on April 21st (in class exam)
All midterms are closed book, closed notes. You are allowed a letter sized cheat sheet both sides with whatever content you wish to put in it.

HomeWorks

List of Topics covered (recorded classroom lectures)

Lectures	Topic	Additional Reading
Jan 12 - Jan 18	Putative framework via example: NEST thermostat, Waymo, face image generation. Supervised, Unsupervised, Reinforcement Learning. Independent variable, covariates, feature vector vs Class label, dependent variable Continuous versus nominal features Classification versus Regression Intro to Mathematical probability theory: Sample space, outcome, sigma algebra of events, probability measure
Jan 19 - Jan 25	Convergence of sets; lim_n sup An and lim_n inf An Some basic theorems Random variable Distribution function, Density function Indicator random variable; Expectation
Jan 26 - Feb 01	Lebesgue integral Conditional distribution Bayes Theorem Independent RV's. Conditional distribution; variance; covariance Started statistical learning theory	Statistical Learning Theory,
Feb 02 - Feb 08	Statistical learning theory continued. Classification, Regression, Density estimation Various Loss functions, Risk functional, and guarantees in idealized scenarios.
Feb 09 - Feb 15	Midterm 1 Empirical Risk minimization principal Weak and Strong law of Large numbers Multivariate regression and Normal Equations
Feb 16 - Feb 22	Convergence of Random variables: convergence in distribution, convergence in probability, almost sure convergence. Finished Multivariate regression and Normal Equations Ridge regression, and Tikhonov regularization Compressed sensing; Basis Pursuit, Basis Pursuit denoissing LASSO
Feb 23 - Mar 01	Brief introduction to Neuroscience, the human brain, the neuron, and Computational Neuroscience McCulloch-Pitts (MCP) neuron, Perceptron Perceptron learning algorithm, and convergence proof Linear separability, Minksy Pappert counterexample	Perceptron Algorithm convergence bound,
Mar 02 - Mar 08	Representational power of Multi-layer Perceptrons Worked out example of the Exclusive-OR network Gradient of a function; Gradient descent/ascent