Foundations for Machine Learning
By External Provider
About this course
This course provides a practical and theoretical tour of the most essential probability distributions that are most often used for modern machine learning and data science. We will explore the fundamental building blocks for modeling discrete events (Bernoulli, binomial, multinomial distributions) and continuous quantities (Gaussian distribution) and discuss the implications of Bayes Theorem. Moreover, we will discuss two perspectives in estimating the model parameters, namely Bayesian perspective and frequentist perspective and learn how to reason about uncertainty in model parameters themselves using the powerful beta and Dirichlet distributions for Bayesian perspective and maximum likelihood estimate for frequentist perspective. By the end of this course, you will have a fluent command of the mathematical "language" needed to understand, build, and interpret probabilistic models.
What you will learn
- How to model data with key distributions, apply Bayes and MLE, and quantify uncertainty via conjugate priors.