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

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

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

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

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

Slides: http://data4sci.com/landing/probability