A training workshop on the mathematical fundamentals required for understanding data science and machine learning techniques. Designed for individuals intending to pursue knowledge in the fields, topics include basic probability theory, understanding expectations and random processes, cluster analysis and linear modelling using standard tools in statistics.

PREREQUISITE : Basic Python or programming skills, and Basic mathematics + statistics (if any)

Lesson 1 Basic Probability Theory
  1.1. Probabilities & Probability Spaces
  1.2. Conditional Probabilities & Bayes Theorem
  1.3. Random Variables & Probability Distributions
  1.4. Multivariate & Joint Probability Distributions
Lesson 2 Expectations & Random Processes
  2.1. Means, Variances & Covariances
  2.2. Understanding Random Processes & Convergence
  2.3. Confidence Intervals
  2.4. Hypothesis Testing
Lesson 3 Cluster Analysis
  3.1. Overview of Cluster Analysis
  3.2. Simple Hierarchical Clustering
  3.3. K-Means
  3.4. Evaluating Clusters: Impurity & Silhouette Scores
Lesson 4 Linear Models
  4.1. Overview of Regression Models
  4.2. Parametric & Non-parametric Estimation
  4.3. Ordinary Least Square (OLS) Estimation
  4.4. Evaluating Regression Models: MSE & R2