Skip to content
No description or website provided.
Jupyter Notebook
Branch: master
Clone or download
Latest commit d767a5c Oct 30, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data updated notebooks Sep 25, 2019
slides updated slides and notebooks Oct 29, 2019
.gitignore gitignore Sep 25, 2019
1. Intuition.ipynb fixed typos Oct 30, 2019
2. Bayesian Statistics.ipynb fixed typos Oct 30, 2019
3. RW.ipynb fixed typos Oct 30, 2019
4. AB Testing.ipynb fixed typos Oct 30, 2019
LICENSE Create LICENSE Jul 3, 2019
README.md Update README.md Oct 19, 2019
d4sci.mplstyle Updated figure style Oct 19, 2019

README.md

Applied Probability Theory From Scratch

Code and slides to accompany the online series of webinars: https://data4sci.com/probability 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

Schedule

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.

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

You can’t perform that action at this time.