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).

 

 

Instructor:

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

 

Office:

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

 

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

 

Office: MRE Building Room 203B

 

Office Hours: To be announced

 

Email: dainis.boumber@gmail.com

 

 


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%

Homework

40%

 

 

  


Calendar

 

 

Dates to remember

Events

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.


Schedule

 

 

 

Dates

Topic

 

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.