18B1WCI634 / 18B11BI611: Machine Learning

This course will introduce fundamental concepts of machine learning.

Class Schedule

  • Batches: 23C11,23J11, 23J12, 23K11, 23I11, 23I12, 23F11
    Day & Time: Tuesday: 15:00 – 16:00 (LT-3) | Friday: 14:00 – 15:00 (LT-2)
  • Batches: 23A17, 23A18, 23A19
    Day & Time: Wednesday: 09:00 – 10:00 (CR-9) | Friday: 12:00 – 13:00 (CR-1)

Instructors

  • Dr. Kushal Kanwar (KLK)(Course Coordinator)
  • Mr. Sandeep Kumar Patel (SKP)

Syllabus

This is a 2-0-0 (L–T–P) course.

  • Unit 1: Introduction
    Learning systems and applications; Concept learning; Hypothesis space; Version space; Candidate Elimination; Inductive bias.
  • Unit 2: Decision Tree Learning
    Decision tree representation; Recursive tree induction; Entropy; Information Gain; Overfitting; Pruning; Linear regression.
  • Unit 3: Artificial Neural Networks
    Biological neuron model; Perceptron; Multilayer neural networks; Backpropagation; Bayesian learning; Naive Bayes; Logistic regression.
  • Unit 4: Support Vector Machine
    Support Vector Machine; Kernel function; Kernel SVM; Instance-based learning; k-Nearest Neighbor algorithm.
  • Unit 5: Genetic Algorithm and Evolutionary Algorithms
    Evolutionary computation; Hypothesis representation; Genetic operators; Fitness function; Selection; Genetic search.
  • Unit 6: Clustering and Unsupervised Learning
    Unsupervised learning; Hierarchical clustering; Agglomerative clustering; k-means clustering; Expectation Maximization; Semi-supervised learning.

Syllabus for TEST-1 Examination

Unit 1
  1. Introduction
    • Definition of learning systems
    • Goals and applications of machine learning
    • Aspects of developing a learning system:
      • Training data
      • Concept representation
      • Function approximation
  2. Inductive Classification
    • The concept learning task
    • Concept learning as search through a hypothesis space
    • General-to-specific ordering of hypotheses
    • Finding maximally specific hypotheses
    • Version spaces
    • List-then-Eliminate
    • Search-Eliminate
    • Candidate elimination algorithm
    • Learning conjunctive concepts
    • Importance of inductive bias
  3. Information Theory
    • Entropy
    • Cross-Entropy
    • KL-Divergence
  4. Loss Functions
    • Regression
    • Classification

Text Books

  1. Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997. ISBN: 0070428077.
  2. Ethem Alpaydin, Introduction to Machine Learning, 2nd Edition, MIT Press.
  3. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, Foundations of Machine Learning, 2nd Edition, MIT Press.
  4. Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms 1st Edition, Cambridge University Press.

Lecture Schedule

Topic Slides
Machine Learning Marking Scheme
Unit-1: Introduction to Machine Learning
Unit-1: Inductive Classification
Unit-1: Information Theory
Unit-1: Loss Function
ML Assignment-1 (Due Date: 07th Feb, 2026)