COSC 6342: Machine Learning, Summer 2018 (Online Course)

General Information


Note: This will be a hybrid-online course. Lectures will be provided as video-recordings

through Blackboard. Students can take the class fully online. Homework and exams will

be provided through Blackboard. Students who wish to attend lectures face-to-face can opt

to do that on every Wednesday (10:00 AM to 12:00 PM).




Ricardo Vilalta (



Multidisciplinary Research and Engineering (MRE) Building Room 203C


Office Hours:

Wednesdays 2:00 PM - 3:00 PM


Class time and room location:

Wednesdays 10:00 AM - 12:00 PM; Building: MH (Melcher Hall); Room: 126



(713) 743-3614




Machine Learning: A Bayesian and Optimization Perspective by Sergios Theodoridis, Elsevier, First Edition, 2015.


Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David; Cambridge University Press, 2014.


The Elements of Statistical Learning by T. Hastie, R. Tibshirani, and J. Friedman; 2nd Edition. Springer, 2009.


Pattern Recognition and Machine Learning by Christopher Bishop; 1st Edition. Springer, 2007.


Pattern Classification by R. Duda, P. Hart, and D. Stork; 2nd Edition, Wiley-Interscience, 2001.



Information TAs (Teacher Assistants)



Dainis Boumber


Office: MRE Building Room 203B


Office Hours: To be announced





Course Description


Machine Learning is the study of how to build computer systems that learn from experience. It is

a subfield of Artificial Intelligence and intersects with statistics, cognitive science, information

theory, and probability theory, among others. The course will explain how to build systems that

learn and adapt using real-world applications from industry and science (e.g., learning to classify

astronomical objects, to predict medical diagnoses, to play chess, etc.).


The class will be self-contained (i.e., I will not assume any previous knowledge); a review session

on probability and information theory will precede those chapters in need of background

knowledge. Main topics include linear discriminants, neural networks, decision trees, support

vector machines, unsupervised learning, reinforcement learning, etc.


For more information visit the course on Blackboard.





Graded Work


2 Midterm Exams










Dates to remember


June 6


1st class

July 4


No Class; Independence Day

June 27

1st Midterm Exam

July 18

2nd Midterm Exam


Note: There is no final exam in this course.








June 6


Introduction to Machine Learning

Probabilistic Learning



June 13


Linear Discriminants

Support Vector Machines



June 20



Decision Trees

Neural Networks



June 27


1st midterm exam



July 4 (online for all)





Ensemble Learning

Unsupervised Learning

Bayesian Networks



July 11



Evolutionary and Stochastic Search

Reinforcement Learning



July 18



2nd midterm exam































 Files for Downloading and Additional Class Information


Connect to the class through Blackboard.