Introduction to Machine Learning
Bruce MacLennan [he/his/him]
Office: Min Kao 550
Hours: MW 2:30–3:30, or make an
Nicholas Van Nostrand
Office: Min Kao 217
Hours: MWF 2:30–3:30, or make an
Classes: 1:25–2:15 MWF in Min Kao 524
Office: Min Kao 205
Hours: TR 10:00–11:00, or make an
Email: zli96 at
This page: http://web.eecs.utk.edu/~bmaclenn/Classes/425
- 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 clustering,
decision trees, neural network learning, statistical learning
methods, Bayesian learning methods, dimension reduction, kernel
methods, and reinforcement learning. Programming assignments
include implementation and hands-on experiments with various
- COSC 425 is not in the Graduate Catalog, and so graduate
students cannot take COSC 425 for graduate credit.
Instead, there is a new course, COSC 522 Machine Learning, which
will be taught this semester.
- COSC 425:
- (RE) Prerequisite(s): Electrical and Computer Engineering 313
or 317 or Mathematics 323; Mathematics 251 or 257.
- This is a 400-level computer science course, and it is taught
at a level appropriate for seniors in computer science. You will
be expected to have the background knowledge of senior CS
students and, of course, to be competent, efficient, and
- 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) [optional]
- Machine Learning Experiments (ch. 19) [optional]
Return to MacLennan’s
to Bruce MacLennan / MacLennan@utk.edu
This page in web.eecs.utk.edu/~bmaclenn/Classes/425
Last updated: 2019-09-13.