Machine Learning Foundations: Probability

Get an in-depth introduction to probability, find out why it’s a prerequisite for machine learning, and learn how to use it to design and implement machine learning algorithms.

Introduction

• Probability for machine learning
• What you should know

1. Introduction to Probability

• Defining probability
• Applications of probability in ML
• Sample space and events
• Random variables
• Examples of probability

2. The Rules of Probability

• Probability of an event
• The sum rule
• The product rule
• The sum rule extended
• Conditional probability
• Total probability

3. The Joint and Marginal Probability

• Joint and marginal probability
• Joint probability tables
• The chain rule for probability

4. Discrete Probability Distributions

• Probability distributions
• Histograms and probability
• Discrete probability distribution
• The binomial distribution
• The Bernoulli distribution
• The Poisson distribution

5. Continuous Probability Distributions

• The continuous probability distribution
• Central limit theorem
• The law of large numbers

6. The Bayes' Theorem

• Introduction to Bayes' theorem
• Example of Bayes' theorem in practice
• Naive Bayes' clasifier

Conclusion

• Next steps