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 31) Programming Assignment 1 has been uploaded. Assignment is due on February 11. Please use LMS for submission.

• (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 to 12 PM
    

  • Teaching Assistant: Muhammad Ahmad (25100076@lums.edu.pk), Office hours: Monday, Thursday: 2 PM to 3 PM
    

  • Teaching Assistant: Osama Ahmad (osama_ahmad@lums.edu.pk), Monday, Wednesday: 10:30 AM to 11:30 AM
    

  • Teaching Assistant: Hamza Rafique (hamza.rafique@lums.edu.pk), Tuesday: 4 PM to 5 PM, Friday: 11 AM to 12 PM
    

  • 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

  • Google Colab Link (Click here), Create your own copy.

• Assignment 1

  • Google Colab Link (Click here), Create your own copy.

• Assignment 2

  • Google Colab Link (Click here), Create your own copy.

  • Download Dataset, Zip Folder Click here

• Assignment 3

  • Part A, Google Colab Link (Click here), Create your own copy.

  • Part B, Google Colab Link (Click here), Create your own copy.

  • Part C, Google Colab Link (Click here), Create your own copy.

• Assignment 4

  • Google Colab Link (Click here), Create your own copy.
    

Quizzes

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

  • Principal Component Analysis (Formulation)

  • Performance evaluation of Classifiers (Notes)

• Week 04

  • Linear Regression (Notes)

  • Polynomial Regression, Bias-Variance Trade-off

• Week 05

  • Regularization, Gradient Descent (Notes)

  • Logistic Regression (Notes)

• Week 06

  • Perceptron Classifier (Notes)

  • Support Vector Machines (SVM) (Notes)

• Week 07

  • Soft SVM, Kernel Trick

• Week 08

  • Bayesian Bayesian Learning: MAP and ML Estimation (Notes)

  • Naïve Bayes Classifier, Text Classification Example

• Week 09-10

  • Neural Networks Introduction, Model, Forward Pass (Notes)

• Week 11

  • Recurrent Neural Networks, Long Short-Term Memory (LSTM) Networks (Notes)

• Week 12

  • Deep Learning Overview, Convolutional Neural Networks (Notes)

• Week 13-14

  • Probabilistic Machine Learning (Notes)

• Week 14

  • Unsupervised Learning, Clustering Overview, K-means Clustering, Agglomerative Clustering (Notes