CS60020 : Foundations of Algorithm Design and Machine Learning Spring 2026

Schedule

Instructor     Partha Pratim Chakrabarti   |   Aritra Hazra
Timings [Slot: D4]     Monday (12:00-13:00) | Tuesday (10:00-12:00) | Thursday (08:00-09:00)
Venue     NC-231 (Nalanda Complex)
MS-Teams Link     Click HERE   [enrollment key/details will be emailed]
Teaching Assistants     Debjyoti Das Adhikary  |  Illa Sai Ravi Teja  |  Saideep Pandey

Notices and Announcements

December 15, 2025

This course is primarily designed as a CORE course for PGDBA students. However, research scholars (MS and PhD students) are allowed to participate in this course. Except these students, we are unable to admit any other UG or PG student to take part in this course. Hence, all requests from students sent via ERP will remain pending without being approved (and thereby those students are requested to switch into other courses before the subject registration deadline expires).

Tentative Coverage

Module I : Foundations of Algorithm Design

Topic Details Duration
Introduction

Problems, Representation, Solution and Landscape

 1 hour
Algorithm Design and Analysis

Recursive Formulation and Data Structuring
Correctness and Optimality (Problem Lower Bounds)
Efficiency (Time and Space Complexity)

6 hours
Divide and Conquer Paradigm

Searching and Sorting
More Example Problems and Analysis
Master Theorem and Complexity Analysis

4 hours
Dynamic Programming

Classical Example Problems and Analysis
String Matching Algorithms
Memo(r)ization and Complexity Analysis

4 hours
Greedy Paradigm

Example Problems (Huffman Coding) and Optimality
Correctness and Complexity Analysis

2 hours
Graph Algorithms

Representation and Traversals
Shortest Paths and Spanning Trees
Heuristic Search and Branch-and-Bound Approach
Constraint Satisfaction and Local Search

5 hours
Intractable (Hard) Problems

P, NP and NP-Complete Problems
Problem Reduction and Hardness

2 hours


Module II : Foundations of Machine Learning

Topic Details Duration
Introduction

The Learning Problem
Feasibility of Learning

2 hours
Decision Tree Learning

Entropy and Information Gain, Gini Index and Impurity
Hypotheses Space and Inductive Bias, Occam's Razor
Overfitting and Prunning

2 hours
Bayesian Learning

MLE and MAP, Naive Bayes and Gaussian Naive Bayes
Bayesian Network and Graphical Models
Expectation-Maximization Algorithm

4 hours
Linear Models

Classification and Regression
Non-linear Transformation
Logistic Regression

2 hours
Artificial Neural Networks

Perceptrons and Multi-Layer Neural Networks
Back-propagation and Gradient Descent
Deep Learning Architectures

2 hours
Performance Evaluation

Accuracy, Precision, Recall, F-Score and ROC
Sampling and Bootstraping
Hypotheses Testing and Cross-Validation

1 hour
Support Vector Machines

Primal-Dual Optimization and Soft Margin
Kernels and Radial Basis Function
Dimensionality Reduction (Principal Component Analysis)

3 hours
Learning Theory

Error and Noise, Training vs. Testing and Validation
Generalization, PAC Learnability and VC Dimensions
Bias and Variance, Overfitting and Regularization

5 hours
Ensemble Learning

Bagging and Boosting
AdaBoost and Random Forest

2 hour
Unsupervised Learning

Clustering Techniques and Evaluation

2 hours
Conclusion

Core Learning Principles

1 hour

Books and References

Module I : Foundations of Algorithm Design
  1. Thomas H. Cormen, Charles E. Lieserson, Ronald L. Rivest and Clifford Stein; Introduction to Algorithms, Fourth Edition, MIT Press/McGraw-Hill, 2022.
  2. Jon Kleinberg and Éva Tardos; Algorithm Design, Pearson, 2005.
  3. Muhammad Hamad Alsuwaiyel; Algorithms: Design Techniques and Analysis, World Scientific Publishers, 2016.
  4. Ellis Horowitz, Sartaj Sahni and Sanguthevar Rajasekaran; Fundamentals of Computer Algorithms, Universities Press, 2008.
  5. Mark Allen Weiss; Data Structures and Algorithm Analysis in C, Pearson Education, 1997.
Module II : Foundations of Machine Learning
  1. Ethem Alpaydin; Introduction to Machine Learning, Fourth Edition, The MIT Press, March 2020.
  2. Tom Mitchell; Machine Learning, First Edition, McGraw Hill, 1997.
  3. Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin; Learning From Data, First Edition, AML Book, 2012.
  4. Christopher Bishop; Pattern Recognition and Machine Learning, First Edition, Springer-Verlag New York, 2006.    [Open-Access]
  5. Stuart Russell and Peter Norvig; Artificial Intelligence: A Modern Approach, 4th Global Edition, Pearson, 2022.

Tests

Marks Distribution:   20% Class-Tests + 30% Mid-Sem + 40% End-Sem + 7% Scribe + 3% Attendance