IST - Advanced Topics in Statistical Learning (2022)

Speakers and Syllabus

 

Speaker

Number of Lectures (1.5 hour for each lecture)

Syllabus

Prof. Ayanendranath Basu
(AB)

4 lectures (6 hours)

  • Multiple linear regression,

  • Regression diagnostics,

  • Variable selection (using Ridge, Lasso, Elastic net),

  • Quantile regression (univariate)

Dr Anirvan Chakraborty
(AC)

4 lectures (6 hours)

  • Nonlinear regression

  • Nonparametric regression using kernels, local polynomials & splines

  • Regression Tree

  • Functional regression (brief idea)

Prof. Anil K. Ghosh
(AKG)

4 lectures (6 hours)

  • Introduction to classification: basic ideas of classifier construction, Bayes rule

  • Parametric classifiers: LDA, QDA and logistic regression

  • Nonparametric classifiers: KNN, CART and kernel discriminant analysis

  • Support vector machines

Dr Shyamal Krishna De
(SKD)

4 lectures (6 hours)

  • Neural networks, ideas of deep learning

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

  • Principle Component Analysis

  • Independent Component Analysis

Dr Kiranmoy Das
(KD)

4 lectures (6 hours)

  • Association measures for categorical data, odds, odds ratio,

  • Generalized linear models,

  • Logistic Regression, Multinomial logit models, Poisson Regression,

  • Graphical Models (idea only)

Prof Samarjit Das
(SD)

4 lectures (6 hours)

  • Introduction to time series: examples of different components including trend and seasonality

  • Estimation of trends and others components

  • Different time series models (incl. AR, MA, ARMA and ARIMA)

  • Forecasting

 Speakers:

  1. Prof. Ayanendranath Basu, ISI, Kolkata (AB)

  2. Prof. Anil K. Ghosh, ISI, Kolkata (AKG)

  3. Prof. Samarjit Das, ISI, Kolkata (SD)

  4. Dr. Anirvan Chakraborty, IISER, Kolkata (AC)

  5. Dr. Kiranmoy Das, ISI, Kolkata (KD)

  6. Dr. Shyamal Krishna De, ISI, Kolkata (SKD)

 

Tutors :

  1. Dr. Raju Maiti, ISI, Kolkata (RM)

  2. Dr. Debashis Chatterjee, Biswa Bharati University, Santiniketan (DC)

  3. Dr. Buddhananda Banerjee, IIT, Kharagpur (BB)

  4. Dr. Satyaki Majumder, IISER, Kolkata (SM)

  5. Dr. Jayabrata Biswas, University of Burdwan (JB)

  6. Dr. Kiranmoy Chatterjee, West Bengal State University, Barasat (KC)

Time Table

 

Tentative Schedule

 

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

 

 

 

 

 

 

11.07.22

Mon

Multiple Linear Regression

(AB)

 

Regression Diagnostics

(AB)

 

Multiple Linear Regression

(AB, SM, BB)

 

Regression Diagnostics

(AB, SM, BB)

12.07.22

Tuesday

Variable Selection

(AB)

 

Quantile Regression

(AB)

 

Variable Selection

(AB, SM, BB)

 

Quantile Regression

(AB, SM, BB)

13.07.22

Wednesday

Nonlinear Regression

(AC)

 

Nonparametric

regression

(AC)

 

Nonlinear Regression

(AC, RM, KC)

 

Nonparametric

regression

(AC, RM, KC)

14.07.22

Thusday

Regression

Tree

(AC)

 

Functional Regression

(AC)

 

Regression

Tree

(AC, RM, KC)

 

Functional Regression

(AC, RM, KC)

15.07.22

Friday

Introduction to

Time Series

(SD)

 

Estimation of

Trends

(SD)

 

Introduction to

Time Series

(SD, RM, BB)

 

Estimation of

Trends

(SD, RM, BB)

16.07.22

Saturday

Different Time Series Models

(SD)

 

Forecasting

(SD)

 

Different Time Series Models

(SD, RM, BB)

 

Forecasting

(SD, RM, BB)

 

 

 

 

SECOND WEEK

 

 

 

 

 

 

18.07.22

Monday

Association measures for categorical data

(KD)

 

 

Generalized linear models

(KD)

 

 

Association measures for categorical data

(KD, JB, KC)

 

Generalized linear models

(KD, JB, KC)

19.07.22

Tuesday

Logistic regression & Poisson reg.

(KD)

 

Introduction to graphical models

(KD)

 

Logistic regression & Poisson reg.

(KD, JB, KC)

 

Introduction to graphical models

(KD, JB, KC)

20.07.22

Wednesday

Introduction to classification

(AKG)

 

Parametric classifiers

(AKG)

 

Introduction to classification

(AKG, DC, SM)

 

Parametric classifiers

(AKG, DC, SM)

21.07.22

Thursday

Nonparametric classifiers

(AKG)

 

Support vector machines

(AKG)

 

Nonparametric classifiers

(AKG, DC, SM)

 

Support vector machines

(AKG, DC, SM)

22.07.2022

(Friday)

Neural netwros

& deep learning

(SKD)

 

Cluster

Analysis

(SKD)

 

Neural netwros

& deep learning

(SKD, DC, JB)

 

Cluster

Analysis

(SKD, DC, JB)

23.07.22

Saturday

Principal component analysis

(SKD)

 

Independent component analysis

(SKD)

 

Principal component analysis

(SKD, DC, JB)

 

Independent component analysis

(SKD, DC, JB)
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