• Skip to primary navigation
  • Skip to main content
  • Theodora Chaspari
  • CV
  • HUman Bio-Behavioral Signals Lab
  • Teaching
    • CSCE 421/633: Machine Learning
    • CSCE 689: Human Behavior Analytics
    • CSCE 689: Human Behavior Analytics (Fall 2018)

Theodora Chaspari

Texas A&M University College of Engineering

CSCE 421/633: Machine Learning

Course information

Instructor: Dr. Theodora Chaspari

Credits: 3

Syllabus (Fall 2021): here

Roadmap (Fall 2021): here

Class final projects (Fall 2017): here

Course description

Machine learning is a sub-field of Artificial Intelligence that gives computers the ability to learn and/or act without being explicitly programmed. Applications of machine learning have permeated many aspects of every-day life and can be found among others in self-driving cars, speech recognition, computer vision, and genomics. Topics include supervised and unsupervised learning (including parametric and non-parametric models, clustering, dimensionality reduction, deep learning), optimization procedures, and statistical inference. The objective of this course is to teach fundamental methods of machine learning with focus on the theoretical underpinnings, practical implementations, and experimentation.

Resources

Lecture 1: Introduction to Machine Learning (slides)

Lecture 2: K-Nearest Neighbor classifier (slides)

Lecture 3: Linear perceptron algorithm (slides)

Lecture 4: Linear regression (slides)

Lecture 5: Logistic regression (slides)

Lecture 6: Multilayer perceptron (slides)

Lecture 7: Deep learning (slides)

Lecture 8: Decision trees and random forests (slides)

Lecture 9: Ensemble learning (slides)

Lecture 10: Dimensionality reduction and feature selection (slides)

Lecture 11: Clustering (slides)

Lecture 12: Explainable AI (slides)

Lecture 13: AI Fairness (slides)

Handouts

Math symbols (handout)

Vector perpendicular to line (handout)

Linear perceptron practice problem (handout)

Linear regression optimization (handout)

Regularized regression (handout)

Maximum likelihood estimation (handout)

Logistic regression optimization (handout)

Regularized logistic regression (handout)

Proof outline of back-propagation (handout)

Example of back-propagation (handout)

Neural networks handout (handout)

Decision trees example (handout)

Regression trees example (handout)

Adaboost example (handout)

PCA proof outline (handout)

Hierarchical clustering example (handout)

GMM example (handout)

 

© 2016–2023 Theodora Chaspari Log in

Texas A&M Engineering Experiment Station Logo
  • State of Texas
  • Open Records
  • Risk, Fraud & Misconduct Hotline
  • Statewide Search
  • Site Links & Policies
  • Accommodations
  • Environmental Health, Safety & Security
  • Employment