Probability for Machine Learning

An introduction to probabilistic models, including random processes and the basic elements of statistical inference.

Skills You Will Gain

  1. The basic structure and elements of probabilistic models
  2. Random variables, their distributions, means, and variances
  3. Probabilistic calculations
  4. Inference methods
  5. Laws of large numbers and their applications
  6. Random processes

Prerequisites

  1. Commitment to invest 23 Hours in 1 Week anytime that works best for you. (Average 3 Hours per day)
  2. An internet-connected computer
  3. Fluent in English

Action Plan

  • Task 1: Review Siraj's 3 month machine learning curriculum
  • Task 2: Join Introduction to Probability - The Science of Uncertainty (EdX)
  • Task 3: Set time on your calendar to complete all tasks this week
  • Task 4: Course Overview
  • Task 5: Probability models and axioms
  • See All Tasks

    Content By

    MIT

    The mission of MIT is to advance knowledge and educate students in science, technology and other areas of scholarship that will best serve the nation and the world in the 21st century.


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