Spring 2022, Tuesday 6:00 pm-8:50 pm, FMH 406
Instructor
Dr. Joerg Kliewer
213 ECEC
Phone: (973) 596-3519
E-mail: jkliewer ATT njit DOTT edu
Office hours: by appointment
Description
This course provides a systematic introduction to pattern recognition and machine learning using information-theoretic performance criteria as guiding principles. Topics covered include linear and Kernel models for classification and regression, sample complexity and VC dimension, probabilistic graphical models and approximate inference.
Prerequisites
Good knowledge of probability and algebra.
Textbooks
O. Simeone. A Brief Introduction to Machine Learning for Engineers. Foundation and Trends in Signal Processing, NOW Publishers, 2018.
Auxiliary reading
- [B] C. Bishop. Pattern Recognition and Machine Learning, Springer, 2006.
- [S] S. Shalev-Shwartz and S. Ben David, Understanding Machine Learning, Cambridge University Press, 2014.
Grading
- Homework 20%
- Exam 40%
- Project 40%
Grading policy: There is no grace period for homework, late submissions cannot be accepted (absolutely no exceptions)
Tentative Schedule
| Week | Topics | Chapter | Auxiliary Reading |
|---|---|---|---|
| 1-2 | Introduction | 1, 2, 3 | B1, B3 |
| 3-4 | Learning and probability | 4 | B2 |
| 5 | Linear models for classification | 5 | B4, B6 |
| 6 | Statistical learning | 6 | S2, S3, S4, S5 |
| 7-8 | Unsupervised learning | 7 | B9 |
| 9-10 | Probabilistic graphical models | 8 | B8 |
| 11-12 | Approximate inference | 9 | B10, B11 |
| 13 | Exam week |