- Introduction to probability: Discrete vs Continuous.
- Funny things on probability.
- Conditional Probability, Bayes Theorem, Law of total probability.
|
- Probability Mass Function vs Probability Density Function.
- Introduction to R programming language and running your programs on R Studio.
- Numerical processing and working with messy data.
|
| Day 2 |
- Probability distribution: Poisson, Exponential, Normal
- Using histograms to check data distribution
|
- Exception handling and identification.
- Lab Session on Distributions and exception handling.
|
| Day 3 |
- Introduction to WLLN and central limit theorem.
- Importance of WLLN in the real world.
|
- Hypothesis testing and Confidence Interval.
- Practical exposure to WLLN and Central limit theorem and hypothesis testing.
|
| Day 4 |
- Introduction to regression: Linear regression, multivariate regression.
- Importance of likelihood and parameter estimation of a linear regression model.
|
- Error/Residual Analysis.
- Lab session on regression and parameter estimation.
|
| Day 5 |
- Introduction to Dynamical systems and Markov chains.
- Modelling real world applications as a Markov Chain.
|
- Building a word prediction engine like Google.
|