Day/Time: | MW 3:30pm -- 3:45pm EST |
Location: | JPM - Room 1202 |
Armand M. Makowski | |
Office: | AVW - 2357 |
Voice: | (301) 405 - 6844 |
Fax: | (301) 314 - 9281 |
Email: | armand@umd.edu |
Day/Time: | MW 9:30am - 10:30am EST |
MW 2:00pm - 3:00pm EST | |
Also by appointment (in office or by zoom) |
ENEE 634 has not been offered in a number of years with earlier editions focusing on the application of adaptive learning and statistical signal processing mostly to problems in communications engineering, e.g., blind equalization and identification (unsupervised learning), antenna array and MIMO signal processing, space-time and space-time-frequency coding, and neural networks (nonlinear adaptive learning).
In Spring 2023 ENEE 634 will be devoted to exploring a number of advanced topics in Machine Learning (ML); see list below. The emphasis will be on understanding the theoretical foundations of several important ideas in ML, both supervised and unsupervised, with some attention given to the needed mathematical tools. This reorientation of the course contents reflects the growing popularity of ML techniques in many application areas, and builds on the course ENEE 633/CMSC 828C (Statistical Pattern Recognition) regularly offered during Fall semesters.
1. | Shai Ben-David and Shai Shalev-Shwartz, Understanding Machine Learning: From Theory to Algorithms. A copy is available through this link | |
Cambridge University Press (2014), Cambridge (United Kingdom) | ||
ISBN 978-1-107-05713-5 |
2. | Martin J. Wainwright, High-Dimensional Statistics -- A Non-Asymptotic Viewpoint | |
Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press (2019), Cambridge (United Kingdom) | ||
ISBN 978-1-108-49082-9 |
3. | Roman Vershynin, High-Dimensional Probability -- An Introduction with Applications to Data Science | |
Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press (2019), Cambridge (United Kingdom) | ||
ISBN 978-1-108-41519-4 |
On occasion lectures notes on certain topics and issues will be made available.
A Formal Learning Model [Chap. 3]
Learning via Uniform Convergence [Chap. 4] -- Additional information concerning concentration inequalities can be found in Chapter 2 of the monograph by Wainwright. See also Chapter 2 of the text by Vershynin.
The VC-Dimension [Chap. 6] -- See also Chapters 4 and 5 of the monograph by Wainwright, and Sections 8.3 and 8.4 of the text by Vershynin.
Nonuniform Learnability [Chap. 7]
The Runtime of Learning [Chap. 8]
Stochastic Gradient Descent [Chap. 14]
Kernel Methods [Chap. 16] -- See also Chapter 12 of the monograph by Wainwright.
Online Learning [Chap. 21]
Complexities [Chap. 26]
Covering Numbers [Chap. 27]
Proof of the Fundamental Theorem of Learning Theory [Chap. 28]
PAC-Bayes [Chap. 31]
Week # 1
Week # 2
The final grade for the course will be based on performance on
01/25/2023 | First day of classes for Spring 2023 | |
01/25/2023 | First class for ENEE 634 | |
05/DD/2023 | Last day of classes for Spring 2023 |