EE 514 (CS 535) Machine Learning

Spring 2025

Syed Babar Ali School of Science and Engineering
Lahore University of Management Sciences

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

• (Jan 21) Welcome to EE 514 (CS 535). Course outline has been posted.
  
  

Administrative Details

• Course Outline (Click to download)
  
  

• Suggested Books:
  

  • (CB) Pattern Recognition and Machine Learning, Christopher M. Bishop

  • (KM) Machine Learning: a Probabilistic Perspective, Kevin Murphy

  • (TM) Machine Learning, Tom Mitchell

  • (HTF) The Elements of Statistical Learning: Data mining, Inference, and Prediction, by Hastie, Tibshirani, Friedman

  • (DM) Information Theory, Inference, and Learning Algorithms, David Mackay

• Office Hours and Contact Information
  

  • Instructor: Zubair Khalid (zubair.khalid@lums.edu.pk), Office hours: Tuesday, Thursday 11 am-12 pm
    

  • Teaching Assistant: Muhammad Ahmad (25100076@lums.edu.pk), Office hours: TBA
    

  • Teaching Assistant: Osama Ahmad (osama_ahmad@lums.edu.pk), Office hours: TBA
    

  • Teaching Assistant: Hamza Rafique (hamza.rafique@lums.edu.pk), Office hours: TBA
    

  • Teaching Assistant: Nadia Shams (nadia.shams@lums.edu.pk), Office hours: TBA
    
    

Grading Distribution

• Programming Assignments and Homeworks, 35 %

• Project, 20 %

• Quizzes (1 per week), 15 %

• Final Exam, 30 %
  

Project Description

• Project Description (Click here to download)
  
  
  

Programming Assignments

• Assignment 0
  

Homeworks

Schedule

• Week 01

  • Course Introduction

  • Machine Learning Overview

  • Supervised Learning: Formulation, Setup, Train-test split, Generalization

  • k-Nearest Neighbor (kNN) Algorithm, Algorithm Formulation