COSC 6342: Machine Learning, Fall 2017

General Information



Ricardo Vilalta (



Multidisciplinary Research and Engineering (MRE) Building Room 203C


Office Hours:

Mondays 11:00 AM - 12:00 PM


Class time and room location:

Mondays and Wednesdays 1:00 - 2:30 PM; Building: CBB; Room: 120



(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

Raymond Sutrisno


Office: MRE Building Room 203B


Office Hours: Wed. 2:30 - 4:00 PM




Office: MRE Building Room 203B


Office Hours: Mo. Wed. 12:00 - 1:00 PM





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


Home works


Final Project










Dates to remember


August 21


No Class

September 4


No Class; Martin Luther King Holiday

October 11

1st Midterm Exam

November 22

No Class; Thanksgiving Holiday


November 29

2nd Midterm Exam


December 4

Final Project Due


Note: There is no final exam in this course.







August 21


August 23

No Class


Introduction to Machine Learning


August 28, 30


Probabilistic Learning


September 4


September 6


No Class; Martin Luther King Holiday


Linear Discriminants


September 11, 13


Decision Trees


September 18, 20


Neural Networks


September 25, 27


Deep Learning


October 2, 4


Support Vector Machines


October 9


October 11


Review Midterm Exam 1


Midterm Exam 1


October 16, 18

Ensemble Learning


October 23, 25

Theoretical Machine Learning


October 30, November 1

Unsupervised Learning


November 6, 8

Bayesian Networks, Graphical Models


November 13, 15


November 20


Evolutionary and Stochastic Search


Reinforcement Learning


November 22


November 27


November 29


December 4

No Class; Thanksgiving Holiday


Review Midterm Exam 2


Midterm Exam 2


Final Project Due




Files for Downloading and Additional Class Information


Connect to the class through Blackboard.