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.