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This course is a part in a series of courses that covers a complete course in probability theory taught in the US colleges.
In this course you will learn the notions of expected value and variance. You will learn how to predict the results of random events and how to predict how much on average the actual results would deviate from your estimate.
If you are looking for an efficient way to learn/review probability theory, this course is for you. With dynamic slides instead of slower handwriting, each hour of the series covers the material of more than a week of regular live lectures! Though it is not a replacement for a live course, it is a fantastic aid.
You may review lectures as many times as you want, skip easy exercises when the material is familiar, choose your own pace! If you need help, I will answer every single question you post on the discussion board. If you are not completely satisfied, I offer a 30 day money back guarantee.
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Section 1: Introduction  

Lecture 1 
Introduction
Preview

02:58  
Section 2: Review  
Lecture 2 
Review: Sample spaces and distribution functions
Preview

02:47  
Lecture 3 
Uniform distribution

01:34  
Lecture 4 
Review: Random variables

02:28  
Lecture 5 
Review: Binomial coefficients

02:38  
Section 3: Classical random variables  
Lecture 6 
Binomial distribution

06:34  
Lecture 7 
Problem solving (easy)

03:42  
Lecture 8 
Geometric random variable

04:03  
Lecture 9 
Problem solving (easy)

03:28  
Lecture 10 
Negative binomial distribution

08:41  
Lecture 11 
Problem solving (easy)

01:13  
Lecture 12 
Poisson distribution

07:14  
Lecture 13 
Problem solving (easy)

03:54  
Lecture 14 
Problem solving (warning)

02:50  
Section 4: Expected value  
Lecture 15 
Expected value (motivation)

02:38  
Lecture 16 
Expected value (definition)

02:15  
Lecture 17 
Problem solving (easy)

03:52  
Lecture 18 
Problem solving (intermediate)
Preview

03:22  
Lecture 19 
Problem solving (advanced)

06:00  
Lecture 20 
Functions of random variables. Problem solving (easy)

04:29  
Lecture 21 
Expected values of sums of random variables

05:02  
Lecture 22 
Expected values of products of random variables

03:44  
Lecture 23 
Expected values of sums and products: Warning

05:28  
Lecture 24 
Expected values of sums of random variables: Proof

03:51  
Lecture 25 
Expected values of products of random variables: Proof

03:13  
Section 5: Variance  
Lecture 26 
Variance of random variables: Motivation

02:53  
Lecture 27 
Variance of a random variables: Definition

01:48  
Lecture 28 
Problem solving (easy)

04:36  
Lecture 29 
Properties of variance

02:54  
Lecture 30 
Problem solving (warning)

01:59  
Lecture 31 
Problem solving (easy)

02:17  
Lecture 32 
Problem solving (intermediate)

04:07  
Lecture 33 
Proof of the first property of the variance

02:07  
Section 6: Expected value and variance of classical random variables  
Lecture 34 
Classical random variables: Binomial random variable

01:10  
Lecture 35 
Problem solving (easy)

02:03  
Lecture 36 
Binomial random variable. Proof of the formulas

04:39  
Lecture 37 
Classical random variables: Geometric random variable

02:02  
Lecture 38 
Problem solving (easy)

02:05  
Lecture 39 
Problem solving (intermediate)
Preview

02:01  
Lecture 40 
Problem solving: Negative binomial random variable

02:31  
Lecture 41 
Classical random variables: Poisson random variable

01:25  
Lecture 42 
Problem solving (easy)

01:12  
Lecture 43 
Problem solving (advanced)

03:37  
Section 7: Thank you!  
Lecture 44 
Thank you!

1 page 
I am a professional mathematician. As a mathematician I worked in US, Mexico, Japan, Canada and Germany. I am an author of 16 research papers published in internationally renowned journals.
I taught courses ranging from middle school to PhD level, including such high level courses as Topological Quantum Field Theory, Moduli spaces of Riemann curves, and Topological Surgery Theory.
What you may expect from me is a well structured course with clearly explained notions and theory.