Problem Framing

Bias, Variance and Optimal Bayes Error

Statistical Analysis

Hypothesis Testing

Analysis of Variance

Machine Learning with Structured Data

Unsupervised Learning

Dimensionality Reduction

Clustring

Principle Component Analysis

Supervised Learning

Linear Regression

Classification

Binary

Multiclass

Logistic Regression

Evaluating the Model Performance

Measures of performance

Optimizing and Satisficing measures

Gradient Descent Algorithm

Semi-supervised Learning

Reinforcement Learning