COSC 6342: Machine Learning, Fall 2017


General Information

 

Instructor:

Ricardo Vilalta (r.vilalta.us@ieee.org)

 

Office:

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

 

Telephone:

(713) 743-3614

 

Readings:

 

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

 

Email: dainis.boumber@gmail.com

 

Office: MRE Building Room 203B

 

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

 

Email: raymond@sutrisno.me

 

 


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.

 


Grading

 

 

Graded Work

Weight

2 Midterm Exams

60%

Home works

 

Final Project

20%

 

20%

 

 

  


Calendar

 

 

Dates to remember

Events

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.

 


Schedule

 

 

Dates

Topic

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.