Introduction to Machine Learning
Bruce MacLennan [he/his/him]
Office: Min Kao 550
Hours: WF 2:30–3:30, or make an
Nicholas Van Nostrand
Office: Min Kao 217
Hours: MWF 1:00–2:00, or make an
Office: Programming Clinic (Min Kao 416)
Hours: TR 11:00–12:30, or make an
Classes: 1:25–2:15 MWF in Min Kao 404
This page: http://web.eecs.utk.edu/~mclennan/Classes/425-528
- 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.
- This is an updated version of COSC 425/528. A Tentative List of Topics
is below, but it may be adjusted during the semester.
- 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.
- 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,
- Homework (occasional)
- Projects (five)
- The projects will count for 90% and the homeworks for the
- Late policy: As competent software engineers, you are
expected to hand in assignments on time.
One day late: –20%, two days last: –40%, three or more days
late: no credit. No exceptions!
- No exams
- Subject to change!
Alpaydin: Introduction to Machine Learning (3rd ed., 2014,
- 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.
- Introduction (ch. 1)
- Supervised Learning (ch. 2)
- Bayesian Decision Theory (ch. 3)
- Parametric Methods (chs. 4–5)
- Dimensionality Reduction (ch. 6)
- Clustering (ch. 7)
- Non-Parametric Methods (ch. 8)
- Decision Trees (ch. 9)
- Neural Networks (chs. 10–11)
- Local Models (ch. 12)
- Kernel Machines (ch. 13)
- Reinforcement Learning (ch. 18)
- Machine Learning Experiments (ch. 19)
Return to MacLennan’s
mail to Bruce MacLennan / MacLennan@utk.edu
This page in web.eecs.utk.edu/~mclennan/Classes/425-528
Last updated: 2018-11-09.