Curriculum

Prerequisites: None

Instructors: Prof. Apurva, Prof. Samar Husain, 

This track is suitable for beginner-level linguistics students who want to do experimental research. It would contain lectures and practice sessions on Statistical data analysis using R and experiment design methods.

The structure and curriculum for this track will be as follows. Two themes will be covered in the five-day workshop: (i) Statistical data analysis using R, and (ii) Experiment design.

Theme 1: Statistical data analysis using R (15 hours) 

1. R programming I: Variables, Loops, Conditionals 

2. R programming II: Plotting and file handling 

3. Statistics: Significance testing, t-test 

4. Statistics: Linear regression 

5. Statistics: Linear mixed models 

Theme 2: Experiment design (15 hours) 

1. Basics of experiment design I 

2. Basics of experiment design II 

3. Experiment building with PCibex 

4. Power analysis and sample size determination 

5. Managing experimental data  

Prerequisites: R programming (e.g., you should be able to write loops, conditional statements, and functions in R)

Instructors: Prof. Himanshu Yadav, Sovan, Bhaj Gobindh Raj 

This track is suitable for students who specifically want to learn Bayesian inference and computational modeling methods for their research, which is an important avenue in today’s research scenario. This track would require that a student already knows R programming. It will consist of two themes: (i) Bayesian data analysis, and (ii) Computational cognitive modeling in the Bayesian framework. 

Theme 1: Bayesian modeling (18 hours)

1. Random variables 

2. Statistical inference using Bayes’ theorem 

3. Parameter estimation 

4. Bayesian regression modeling 

5. Model comparison 

6. Bayesian hierarchical models 

Theme 2: Computational cognitive models (12 hours)

1. Stan programming 

2. Writing models in R

3. TBA