Introduction to Probability
Overview
Probability and statistics help to bring logic to a world replete with randomness and uncertainty. This course will give you tools needed to understand data, science, philosophy, engineering, economics, and finance. You will learn not only how to solve challenging technical problems, but also how you can apply those solutions in everyday life.
With examples ranging from medical testing to sports prediction, you will gain a strong foundation for the study of statistical inference, stochastic processes, randomized algorithms, and other subjects where probability is needed.
Syllabus
- Unit 0: Introduction, Course Orientation, and FAQ
- Unit 1: Probability, Counting, and Story Proofs
- Unit 2: Conditional Probability and Bayes' Rule
- Unit 3: Discrete Random Variables
- Unit 4: Continuous Random Variables
- Unit 5: Averages, Law of Large Numbers, and Central Limit Theorem
- Unit 6: Joint Distributions and Conditional Expectation
- Unit 7: Markov Chains
Taught by
Joseph Blitzstein
Related Courses
-
Probability - The Science of Uncertainty and Data
Massachusetts Institute of Technology
4.9 -
Introduction to Stochastic Processes
Indian Institute of Technology Bombay, NPTEL
-
Probability: Basic Concepts & Discrete Random Variables
Purdue University
-
Introduction to Probability (with examples using R)
NPTEL, Indian Statistical Institute
-
Probability: Distribution Models & Continuous Random Variables
Purdue University
-
Introduction to Probability, Statistics, and Random Processes
University of Massachusetts Amherst
3.3
Reviews
5.0 rating, based on 1 reviews
-
Anonymous
Anonymous is taking this course right now.
It was well written good use of graphics to demonstrate. Very exciting I have always wanted to further my skills and this course has done just that opening the door for further learning
Never Stop Learning!
