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EE 514 (CS 535) Machine Learning
Spring 2025
Course Overview
Machine learning (ML) studies the design and development of algorithms that learn from the data and improve their performance through experience. ML refers to a set of methods and that help computers to learn, optimize and adapt on their own. ML has been employed to devise algorithms for diverse applications including object detection or identification in computer vision, sentiment analysis of speaker or writer, detection of disease and planning of therapy in healthcare, product recommendation in e-commerce, learning strategies for playing games, recommending movies to customers, speech recognition systems, fraudulent transaction detection or loan application approval in banking sector, to name a few.
This course provides a thorough introduction to the theoretical foundations and practical applications of ML. We will learn fundamental algorithms in supervised learning and unsupervised learning. We will not only learn how to use ML methods and algorithms but will also try to explain the underlying theory building on mathematical foundations. While reviewing the several problems and algorithms to carry out classification, regression, clustering, dimensionality reduction, we will focus on the core fundamentals which unify all the algorithms. The theory discussed in class will be tested in assignments, quizzes and exams.
Announcements
Administrative Details
Grading Distribution
Programming Assignments and Homeworks, 35 %
Project, 20 %
Quizzes (1 per week), 15 %
Final Exam, 30 %
Project Description
Programming Assignments
Assignment 0
Assignment 1
Homeworks
Schedule
Week 01
Course Introduction
Machine Learning Overview (Notes)
Supervised Learning: Formulation, Setup, Train-test split, Generalization (Notes)
Week 02
k-Nearest Neighbor (kNN) Algorithm, Algorithm Formulation (Notes)
Curse of Dimensionality and Principal Component Analysis (Notes)
Week 03
Week 04
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