Welcome to Online Study
Table of Contents
R Programing
- R Programing
- Control Flows
- Functions
- Vectors
- Matrices and Arrays
- Strings, Factors, Dates
- Lists
- DataFrames
- Classes
- Solutions
Data Manipulation
- Map-Reduce
- Mutation
- Aggregation
- Filtering
Data Cleaning
- String Manipulation
  - text cleaning
  - regex
- Date Manipulation
Data Wrangling
- Lapply, sapply, tapply, rapply, apply
- aggregate, by, merge
- Data Reshaping-reshape, stack
tidyverse
- dplyr, tidyr, purrr
- tidyverse
- strings
- Questions
Optimization
- Root finding Algorithms
- Minimization
- Numerical Differentiation
- Optim, optimise, uniroot
- MLE
- nls
- Integration
- Expectation Maximization
Data Modelling
- Introduction
- Fitting Distributions
- Supervised Learning
  - Regression
    - Linear Regression
    - Ridge Regression
    - LASSO Regression
    - Generalized Linear Models(GLM)
      - Logistic Regression
      - Probit Regression
      - Poisson Regression
      - Beta Regression
  - Classification
    - Naive Bayes
    - KNN
    - Logistic
    - Neural Networks
    - LDA
    - QDA
    - SVM
    - Boosting and Regression Trees
- Unsupervised Learning
  - Clustering
    - K means Clustering Algorithm
    - Spectral Clustering
    - EM clustering

Introduction

This is the idea of fitting a specific model to the data. This model can then be used predict a new observation that was previously in the data. We aim at quantifying the relationship between some variables of interest that are not random and the random variables. A model can be described as either a mathematical formula, or even a generic relation between the variables.