EE 514 (CS 535) Machine Learning

Spring 2021

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.


  • (Jan 09) Welcome to EE 514 (CS 535). Course outline has been posted.

Course Activities Calendar

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 (, Office hours: Wednesday 5-6 pm

    • Teaching Assistant: Rabeeya Hamid (, Office hours: Wednesday 4-5 pm

Grading Distribution

  • Programming Assignments and Homeworks, 25 %

  • Project, 10 %

  • Quizzes (1-2 per week), 20 %

  • Mid-Exam and/or Mid-Viva, 20 %

  • Final Exam, 25 %

Lecture Notes and Exams


Assignment Solutions
Assignments 01 Solutions
Assignments 02 Solutions
Assignments 03 Solutions
Assignments 04 Solutions
Assignments 05 Solutions


Number Quiz Solutions
Quiz 01 pdf Solutions
Quiz 02 pdf Solutions
Quiz 03 pdf Solutions
Quiz 04 pdf Solutions
Quiz 05 pdf Solutions
Quiz 06 pdf Solutions
Quiz 07 pdf Solutions
Quiz 08 pdf Solutions
Quiz 09 pdf Solutions
Quiz 10 pdf Solutions
Quiz 11 pdf Solutions
Quiz 12 pdf Solutions
Quiz 13 pdf Solutions
Quiz 14 pdf Solutions

Course Topics

  • Introduction

  • Regression

  • ML Pipeline

  • Classification

  • Statistical Decision Theory

  • Linear Regression

  • Non-linear Regression

  • Bias-variance tradeoff

  • Linear Classification

  • Indicator Regression

  • Dimensionality Reduction, PCA, LDA

  • Naive Bayes

  • Logistic Regression

  • Perceptron

  • SVM

  • Decision Trees

  • Bagging, boosting, stacking

  • Neural Networks, Backpropagation

  • Training Deep Neural Networks

  • Convolutional neural networks intro

  • Recurrent Neural Networks

  • ML and MAP Estimation Theory

  • Bayesian Learning and Bayesian Linear Regression

  • Kernel Methods and Gaussian Process

  • K-means Clustering

  • Computational Learning Theory (Time Permitting)