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Applied Probability Theory From Scratch

Code and slides to accompany the online series of webinars: by Data For Science.

Recent advances in Machine Learning and Artificial Intelligence have result in a great deal of attention and interest in these two areas of Computer Science and Mathematics. Most of these advances and developments have relied in stochastic and probabilistic models, requiring a deep understanding of Probability Theory and how to apply it to each specific situation

In this lecture we will cover in a hands-on and incremental fashion the theoretical foundations of probability theory and recent applications such as Markov Chains, Bayesian Analysis and A/B testing that are commonly used in practical applications in both industry and academia


Basic Definitions and Intuition

  • Understand what is a probability
  • Calculate the probability of different outcomes
  • Generate numbers following a specific probability distribution
  • Estimate Population sizes from a sample

Random Walks and Markov Chains

  • Simulate a random walk in 1D
  • Understand random walks on networks
  • Define Markov Chains
  • Implement PageRank

Bayesian Statistics

  • Understand conditional Probabilities
  • Derive Bayes Theorem
  • Understand how to Update a Belief

A/B Testing

  • Understand Hypothesis Testing
  • Measure p-values
  • Compare the likelihood of two outcomes.


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