NCMW - Introduction to Statistical Learning Techniques (2024)

Speakers and Syllabus

 

Speaker

Number of Lectures
(1.5 hour for each lecture)

Syllabus

Dr. Arindam Chatterjee (ArC)

Linear Regression

4 lectures (6 hours)

  • Linear regression

  • Penalization and variable selection

  • LASSO-I

  • LASSO-2

Dr. Anirvan Chakraborty (AnC)

Nonparametric Regression

4 lectures (6 hours)

  • Nonlinear regression

  • Nonparametric regression using kernels, local polynomials & splines

  • Wavelet methods

  • Regression Tree

 

Dr. Subhajit Dutta (SD)

Supervised Classification I

4 lectures (6 hours)

  • Introduction to machine learning: supervised, unsupervised and semi-supervised classification.

  • Parametric classifiers: LDA, QDA, Regularized discriminant analysis

  • Classifiers based on nonparametric density estimates, kernel discriminant analysis and k-nearest neighbours

  • Classification tree and Random Forest.

 

Dr. Soham Sarkar (SS)

Supervised Classification II

4 lectures (6 hours)

  • Kernel Methods

  • Support Vector Machines

  • Neural Networks-I

  • Neural Networks-II

 

Dr. Shyamal Krishna De (SKD)

Unsupervised

Classification

4 lectures (6 hours)

  • Principle Component Analysis (PCA) and kernel PCA

  • Cluster analysis: k-means clustering and hierarchical clustering,

  • Spectral clustering

  • Multidimensional Scaling and Self-Organizing Maps

 

Dr. Minerva Mukhopadhyay (MM)

Bayesian Methods in Machine Learning

4 lectures (6 hours)

  • Bayesian Variable Selection

  • Sliced Inverse Regression

  • Variable Selection in Classification

  • Bayesian Nonparametric Methods

 

 Speakers:

    1. Dr.  Anirvan Chakraborty, IISER, Kolkata (AnC)
    2. Dr.  Arindam Chatterjee, ISI, Delhi (ArC)
    3. Dr.  Minerva Mukhopadhyay, IIT, Kanpur (MM)
    4. Dr.  Shyamal Krishna De, ISI, Kolkata (SKD)
    5. Dr.  Subhajit Dutta, IIT, Kanpur (SD)
    6. Dr.  Soham Sarkar, ISI, Delhi (SS)
Tutors :      
    1. Dr. Buddhananda Banerjee, IIT, Kharagpur (BB)
    2. Mr. Bilol Banerjee, ISI, Kolkata (BLB)
    3. Dr. Debashis Chatterjee, Biswa Bharati University, Santiniketan (DC)
    4. Dr. Raju Maiti, ISI, Kolkata (RM)
    5. Mr. Sourav Chakrabarty, ISI, Kolkata (SC)
    6. Dr. Satyaki Majumder, IISER, Kolkata (SM)

 

 

Time Table

 

Date

Lecture 1

10:00-11:30

Tea

11:30-12:00

Lecture 2

12:00-1:30

Lunch

1:30-3:00

Tutorial 1

3:00-4:00

Tea

4:00-4:30

Tutorial 2

4:30-5:30

 

 

 

 

FIRST WEEK

 

 

 

 

 

 

01.07.24

Mon

Linear Regression

(ArC)

 

Penalization and Variable Selection

(ArC)

 

Linear Regression

(ArC, DC, BB)

 

Penalization and Variable Selection

(ArC, DC, BB)

02.07.24

Tuesday

LASSO I

(ArC)

 

LASSO II

(ArC)

 

LASSO

(ArC, DC, BB)

 

LASSO

(ArC, DC, BB)

03.07.24

Wednesday

Nonlinear Regression

(AnC)

 

Nonparametric

Regression

(AnC)

 

Nonlinear Regression

(AnC, SM, SC)

 

Nonparametric

Regression

(AnC, SM, SC)

04.07.24

Thursday

Wavelet Methods

(AnC)

 

Regression Tree

(AnC)

 

Wavelet Methods

(AnC, SM, SC)

 

Regression Tree

(AnC, SM, SC)

05.07.24

Friday

Introduction to

Machine Learning

(SD)

 

Parametric

Classifiers

(SD)

 

Introduction to

Machine Learning

(SD, RM, SM )

 

Parametric Classifiers

(SD, RM, SM)

06.07.24

Saturday

Classifiers based on nonparametric density estimates

(SD)

 

Classification tree

and

Random forest

(SD)

 

Classifiers based on nonparametric density estimates

(SD, RM, SM)

 

Classification tree

and

Random forest

(SD, RM, SM)

 

 

 

 

SECOND WEEK

 

 

 

 

 

 

08.07.24

Monday

Kernel Methods

(SS)

 

 

Support Vector Machines

(SS)

 

 

Kernel Methods

(SS, BLB, DC)

 

Support Vector Machines

(SS, BLB, DC)

09.07.24

Tuesday

Neural Networks I

(SS)

 

Neural Networks II

(SS)

 

Neural Networks I

(SS, BLB, DC)

 

Neural Networks II

(SS, BLB, DC)

10.07.24

Wednesday

Principal Component Analysis

(SKD)

 

Cluster

Analysis

 

(SKD)

 

Principal Component Analysis

(SKD, SC, BLB)

 

Cluster

Analysis

 

(SKD, SC, BLB)

11.07.24

Thursday

Spectral Clustering

 

(SKD)

 

Multidimensional Scaling and Self-organizing Maps (SKD)

 

Spectral Clustering

 

(SKD, SC, BLB)

 

Multidimensional Scaling and Self-organizing Maps (SKD, SC, BLB)

12.07.24

(Friday)

Bayesian Variable

Selection

(MM)

 

Sliced Inverse Regression

(MM)

 

Bayesian Variable

Selection

(MM, RM, BB)

 

Sliced Inverse Regression

(MM, RM, BB)

13.07.24

Saturday

Variable

Selection in Classification (MM)

 

Bayesian Nonparametric Methods

(MM)

 

Variable

Selection

in Classification

(MM, RM, BB)

 

Bayesian Nonparametric

Methods

(MM, RM, BB)

 

 

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