PHILLIP DELEON
University Executive Leader • Chief Research Officer • Professor of Electrical Engineering
Course

Pattern Recognition & Machine Learning

Statistical pattern classification supervised and unsupervised learning, feature selection and extraction, clustering, image classification and syntactical pattern recognition.

Graduate Electrical Engineering Communications/Signal Processing Area
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Course information

New Mexico State University EE565 Pattern Recognition & Machine Learning (3 credits).

Prerequisites

Recommended preparation is EE210 or equivalent probability and statistics and linear algebra courses. Students must have programming competency in MATLAB, Python, or other language appropriate for PRML.

Textbooks

  • Pattern Recognition and Machine Learning by Christopher Bishop, Springer, 2007 (ISBN 978-0387310732)

Course Objectives

The objective of this course is to gain an understanding of the methods used in PRML:

  1. Density estimation methods
  2. Linear models for regression and classification
  3. Neural networks and kernel methods
  4. Support Vector Machines (SVMs) and Relevance Vector Machines (RVMs)
  5. Graphical models and clustering
  6. Mixture models and expectation maximization (EM)
  7. Principal component analysis (PCA)

This objective is achieved through a graduate-level treatment of pattern recognition and machine learning including both theoretical and experimental work.

Course materials

New Mexico State University EE565 Pattern Recognition & Machine Learning lecture notes, homework and solutions, laboratories, and exams.

  • Lecture Notes
Lecture 1 Lecture 2 Lecture 3 Lecture 4 Lecture 5 Lecture 6 Lecture 7 Lecture 8 Lecture 9 Lecture 10 Lecture 11 Lecture 12 Lecture 13 Lecture 14 Lecture 15 Lecture 16 Lecture 17 Lecture 18 Lecture 19 Lecture 20 Lecture 21 Lecture 22 Lecture 23 Lecture 24 Lecture 25
  • Projects and Solutions
Project 1 CompanionFiles1.zip Solution 1 Project 2 CompanionFiles2.zip Project 3 Solution 3 Project 4 CompanionFiles4.zip Solution 4 Project 5 Solution 5 Project 6 CompanionFiles6.zip Solution 6

This page is intended as a stable home for official course documents and high-level information.

Emphasis
Statistical Foundations of Machine Learning
  • Probability theory and random variables
  • Bayesian decision theory
  • Likelihood, prior, and posterior distributions
  • Maximum likelihood and MAP estimation
  • Bias–variance tradeoff
  • Overfitting, regularization, and model selection
Skills
Supervised Learning Methods
  • Linear regression and classification
  • Logistic regression and generalized linear models
  • Support Vector Machines (SVMs)
  • k-Nearest Neighbors (k-NN)
  • Decision trees and ensemble methods
  • Neural networks and deep learning fundamentals
    Preparation
    Unsupervised Learning & Advanced Topics
    • Clustering methods (k-means)
    • Gaussian mixture models (GMMs)
    • Dimensionality reduction (PCA, LDA)
    • Feature extraction and representation learning
    • Model evaluation and performance metrics
    • Applications in pattern recognition and data science