Syllabus
Unit I
Introduction to machine learning, Types of Machine learning, features, class boundary, Training, Validation & Testing, Generalization – underfit, regular fit and overfit; Loss /Cost function; Issues of Explainability, algorithmic bias, data and algorithmic privacy. Curse of Dimensionality, Dimensionality Reduction Techniques – Principal component analysis, Linear Discriminant Analysis, Feature selection – sequential & bi-directional. K-Nearest Neighbour classifier, Regression: Linear, logistic, introduction to Regularization- LASSO, Ridge. Classifier Performance metrics – precision, recall, accuracy, f-score, AUC, Regression Performance metrics –RMSE, MAPE, R2 Score.
Unit II
Supervised learning: Decision Trees, Support vector machines, Naive Bayes, Markov model, Hidden Markov Model, Artificial Neural Network – Perceptron & MLP with learning algorithms, Parameter Estimation: MLE and Bayesian Estimate, Expectation Maximization.
Unit III
Unsupervised learning- Clustering – hierarchical & agglomerative, K means algorithm, cluster evaluation – Elbow technique; Ensemble learning – Bagging and boosting- Adaboost, Random Forest. Introduction to Reinforcement learning. Implementation of machine learning algorithms on real world problems and analyze their performance by tuning hyper parameters.
Objectives and Outcomes
Course Objectives
Machine learning covers significant ground in various verticals – including image recognition, medicine, cyber security, facial recognition, business analytics etc. Study of ML lays a strong foundation for the study on NLP, Deep Learning, Reinforcement Learning, Graphical Models, AI, Predictive Analytics etc.
- The aim of this course is to provide foundational knowledge in machine learning.
- The students will learn to implement, train and validate the machine learning models and understand the recent algorithms in machine learning through case studies.
Pre-Requisite(s): 23MATXXX Linear Algebra, 23MATXXX Probability and Random Processes
Course Outcomes
CO1: Understand the fundamental concepts, issues and challenges of machine learning.
CO2: Implement machine learning algorithms using programming tools and provide solution to real world applications.
CO3: Apply machine learning algorithms for parameter estimation or prediction problem.
CO4: Apply supervised learning techniques for classification problem and analyse their performance.
CO5: Apply un-supervised and ensemble learning techniques to solve a given problem.
CO-PO Mapping
| PO/PSO |
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO10 |
PO11 |
PO12 |
PSO1 |
PSO2 |
| CO |
| CO1 |
3 |
2 |
2 |
3 |
|
|
|
|
|
|
|
|
3 |
2 |
| CO2 |
3 |
2 |
3 |
2 |
3 |
1 |
|
3 |
3 |
3 |
|
3 |
3 |
3 |
| CO3 |
3 |
2 |
2 |
2 |
|
|
|
|
|
|
|
|
3 |
3 |
| CO4 |
3 |
2 |
2 |
2 |
|
|
|
|
|
|
|
|
3 |
3 |
| CO5 |
3 |
2 |
2 |
2 |
|
|
|
|
|
|
|
|
3 |
3 |
Evaluation Pattern
Evaluation Pattern: 70:30
| Assessment |
Internal |
End Semester |
| Midterm |
20 |
|
| *Continuous Assessment (Theory) (CAT) |
10 |
|
| *Continuous Assessment (Lab) (CAL) |
40 |
|
| **End Semester |
|
30 (50 Marks; 2 hours exam) |
*CAT includes Quizzes and Tutorials
*CAL – Can be Lab Assessments, Project, Case Study and Report
**End Semester can be theory examination/ lab-based examination/ project presentation
Text Books / References
Textbook(s)
Tom Mitchell, “Machine Learning”. McGraw Hill; ISBN: 9781259096952, 9781259096952; 2017.
Reference(s)
Kevin P. Murphey. “Machine Learning”, a probabilistic perspective. MIT Press, 2012.
Christopher M Bishop. “Pattern Recognition and Machine Learning”. Springer 2010.
Richard O. Duda, Peter E. Hart, David G. Stork. “Pattern Classification”. Wiley, Second Edition;2007.
Trevor Hastie, Robert Tibshirani, Jerome Friedman. “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”. Springer, 2017.
