COSC 425/528

Introduction to Machine Learning

Fall 2017

Instructor:
Bruce MacLennan [he/his/him]
Phone: 974-0994
Office: Min Kao 550
Hours: WF 2:30–3:30, or make an appointment
Email: maclennan@utk.edu

TAs:
Andrew August
Office: Min Kao 350
Hours: TR 1:30–2:30, or make an appointment
Email: aaugust5 at vols.utk.edu

Yongli Zhu
Office: Min Kao 206
Hours: WF 3:30–4:30, or make an appointment
Email: yzhu16 at vols.utk.edu

Classes: 1:25–2:15 MWF in Min Kao 404

This page: http://web.eecs.utk.edu/~mclennan/Classes/425-528


Information


Description

COSC 425:
Machine learning is concerned with computer programs that automatically improve their performance through experience. This course covers the theory and practice of machine learning from a variety of perspectives. We cover topics such as learning decision trees, neural network learning, statistical learning methods, genetic algorithms, Bayesian learning methods, explanation-based learning, and reinforcement learning. Programming assignments include hands-on experiments with various learning algorithms.
COSC 528:
Theoretical and practical aspects of machine learning techniques that enable computer systems to learn from experience. Methods studied include concept learning, decision tree learning, neural networks, Bayesian learning, instance-based learning, genetic algorithms, rule learning, analytical learning, and reinforcement learning.
N.B.
This is an updated version of COSC 425/528, which has not been taught in five years. A Tentative List of Topics is below, but it may be adjusted during the semester.

Prerequisites

COSC 425:
(RE) Prerequisite(s): 302; Electrical and Computer Engineering 313 or Mathematics 323. Comment(s): Prior knowledge may satisfy prerequisite with consent of instructor.
COSC 528: 
Recommended Background: 302, 311, Mathematics 251; and Mathematics 323, or Electrical and Computer Engineering 313.
N.B.
You will be programming at least six machine learning algorithms. I recommend python or Matlab/Octave, but C++ and Java are also acceptable. If you want to use another language, please contact me.

Grading


Text

Alpaydin: Introduction to Machine Learning (3rd ed., 2014, MIT Press).



Accommodations

For Students with Disabilities
Students who have a disability that requires accommodation(s) should make an appointment with the Office of Disability Services (974-6087) to discuss their specific needs as well as schedule an appointment with me during my office hours.
Name and Pronoun Accommodations
If you use a name and/or pronouns other than what is in the course roll, please email me with the name and/or pronouns that you would like me to use and I will be glad to accommodate this request.

Tentative List of Topics

We will spend a week or two on each of these topics, which correspond to the indicated chapters in the textbook.

  1. Introduction (ch. 1)
  2. Supervised Learning (ch. 2)
  3. Bayesian Decision Theory (ch. 3)
  4. Parametric Methods (chs. 4–5)
  5. Dimensionality Reduction (ch. 6)
  6. Clustering (ch. 7)
  7. Non-Parametric Methods (ch. 8)
  8. Decision Trees (ch. 9)
  9. Neural Networks (chs. 10–11)
  10. Local Models (ch. 12)
  11. Kernel Machines (ch. 13)
  12. Reinforcement Learning (ch. 18)
  13. Machine Learning Experiments (ch. 19)

Additional Information





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Last updated:  2017-08-28.