Course Contents

• Unit 1 - Basics
  • Introduction to machine learning - different forms of learning; Basics of probability theory, linear algebra and optimization.


• Unit 2 – Regression Analysis
  • Linear regression, ridge regression, Lasso, Bayesian regression, regression with basis functions.


• Unit 3 – Classification Methods
  • Linear Discriminant Analysis, Logistic regression, Perceptrons, Large margin classification, Kernel methods, Support Vector Machines, Classification and Regression Trees, Multilayer Perceptrons and Back propagation.


• Unit 4 - Graphical Models
  • Bayesian Belief Networks, Markov Random Fields, Exact inference methods, approximate inference methods.


• Unit 5 – Ensemble Methods
  • Boosting - Adaboost, Gradient Boosting; Bagging - Simple methods, Random Forest.


• Unit 6 – Computational Learning Theory
  • PAC Learning, VC Dimension, Bias/Variance Tradeoff.


• Unit 7 – Clustering
  • Partitional Clustering - k-means, k-medoids; Hierarchical Clustering - Agglomerative, Divisive, Distance measures; Density based clustering - DBScan; Spectral clustering


• Unit 8 – Frequent Pattern Mining
  • Apriori Algorithm; FP-Growth

References

Textbooks

T. Hastie, R.Tibshirani, J. Friedman, "The Elements of Statistical Learning: Data Mining, Inference and Prediction", 2nd Edition, Springer Series in Statistics, 2009.

C. M. Bishop. "Pattern Recognition and Machine Learning," Springer, 2006.

J. Han and M. Kamber. "Data Mining: Tools and Techniques," 3rd Edition, Morgan Kaufmann Press, 2012.

K. R. Murphy. "Machine Learning - A Probabilistic Perspective", 1st Edition, The MIT Press, 2012.

Lecture Slides

1	Introduction
2	Machine Learning - Definitions
3	Least Square Regression
4	Overview of Supervised Learning - Part I
5	Overview of Supervised Learning - Part II
6	Linear Regression and Shrinkage methods
7	Pattern Recognition
8	Gaussian Mixture Models
9	Classification Methods
10	Clustering Methods
11	Spectral Clustering
12	Ensemble Methods - Part 1
13	Graphical Models - Part 1
14	SVM
15	Logistic Regression

Assignment

Software Assignment 1

Software Assignment 2

Prerequisites/Basics

1	Linear Algebra - overview
2	Introduction to Matrices
3	Matrix Multiplication
4	Vectors and Matrix Norms
5	Matrix Inverse and Transpose
6	Determinants
7	EigenValues and EigenVectors
8	Positive Semidefinite Matrices
9	Basics of Probability
10	Permutations and Combinations
11	Random Variables and their Distributions
12	Discrete Random Variables
13	Discrete Distributions
14	Continuous Random Variables

Schedule

Marks Distribution

• Quiz x 2 : 20 - 30
• Software Assignments : 20 - 25
• End Trimester : 40 - 50

Logistic Details

• TA: Sonam Gupta (cs18d005@cse.iitm.ac.in)
  
• TA: Arti Keshari (cs19s008@cse.iitm.ac.in)
  

Course Handout
       

Important Dates (Tentative)