ECE 754: Statistical Machine Learning and Pattern Recognition

Spring 2021, Tuesday 6:00 pm-8:50 pm, synchronous online

Dr. Joerg Kliewer
213 ECEC
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.

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.


  • 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

WeekTopicsChapterAuxiliary Reading
1-2Introduction1, 2, 3B1, B3
3-4Learning and probability4B2
5Linear models for classification5B4, B6
6Statistical learning6S2, S3, S4, S5
7-8Unsupervised learning7B9
9-10Probabilistic graphical models8B8
11-12Approximate inference9B10, B11
13Exam week