Machine Learning Foundations : Probability and Statistics
This course is available as a part of subscription plans
1. Probability & Information Theory
This class, Probability & Information Theory, introduces the mathematical fields that enable us to quantify uncertainty as well as to make predictions despite uncertainty. These fields are essential because machine learning algorithms are both trained by imperfect data and deployed into noisy, real-world scenarios they haven’t encountered before.
Through the measured exposition of theory paired with interactive examples, you’ll develop a working understanding of variables, probability distributions, metrics for assessing distributions, and graphical models. You’ll also learn how to use information theory to measure how much meaningful signal there is within some given data. The content covered in this class is itself foundational for several other classes in the Machine Learning Foundations series, especially Intro to Statistics and Optimization.
Over the course of studying this topic, you'll:
2. Intro to Statistics
This class, Intro to Statistics, builds on probability theory to enable us to quantify our confidence about how distributions of data are related to one another.
Through the measured exposition of theory paired with interactive examples, you’ll develop a working understanding of all of the essential statistical tests for assessing whether data are correlated with each other or sampled from different populations -- tests which frequently come in handy for critically evaluating the inputs and outputs of machine learning algorithms. You’ll also learn how to use regression to make predictions about the future based on training data.
The content covered in this class builds on the content of other classes in the Machine Learning Foundations series (linear algebra, calculus, and probability theory) and is itself foundational for the Optimization class.
Over the course of studying this topic, you'll:
Dr Jon Krohn
Dr. Jon Krohn
1: Introduction to Probability
2: Distributions in Machine Learning
3: Information Theory
4: Frequentist Statistics
5: Regression
6: Bayesian Statistics