Spring 2021, Tuesday 6:00 pm-8:50 pm, synchronous online
Dr. Joerg Kliewer
Phone: (973) 596-3519
E-mail: jkliewer ATT njit DOTT edu
Office hours: by appointment
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.
Good knowledge of probability and algebra.
O. Simeone. A Brief Introduction to Machine Learning for Engineers. Foundation and Trends in Signal Processing, NOW Publishers, 2018.
- [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.
- Homework 20%
- Exam 40%
- Project 40%
Grading policy: There is no grace period for homework, late submissions cannot be accepted (absolutely no exceptions)
|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|
|9-10||Probabilistic graphical models||8||B8|
|11-12||Approximate inference||9||B10, B11|