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MIT 6.042J: Mathematics for Computer Science →0/22
1

Introduction and Proofs (MIT 6.042J, Lecture 1)

44:09
2

Mathematical Induction (MIT 6.042J, Lecture 2)

1:19:25
3

Strong Induction (MIT 6.042J, Lecture 3)

1:22:00
4

Number Theory I (MIT 6.042J, Lecture 4)

1:20:24
5

Number Theory II (MIT 6.042J, Lecture 5)

1:18:45
6

Graph Theory and Coloring (MIT 6.042J Mathematics for Computer Science, Lecture 6)

1:22:51
7

Matching Problems (MIT 6.042J Mathematics for Computer Science, Lecture 7)

1:22:40
8

Graph Theory II: Minimum Spanning Trees (MIT 6.042J, Lecture 8)

1:23:26
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Lecture 9: Communication Networks (6.042J)

1:23:25
10

Lecture 10: Graph Theory III (6.042J)

1:22:50
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Lecture 11: Relations, Partial Orders, and Scheduling (6.042J)

1:04:00
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Lecture 12: Sums (6.042J)

1:18:22
13

Lecture 13: Sums and Asymptotics (6.042J)

1:23:40
14

Divide and Conquer Recurrences (MIT 6.042J, Lecture 14)

1:22:46
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Lecture 15: Linear Recurrences (6.042J)

1:18:20
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Lecture 16: Counting Rules I (6.042J)

1:20:02
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Lecture 17: Counting Rules II (6.042J)

1:25:24
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Lecture 18: Probability Introduction (6.042J)

1:23:56
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Lecture 19: Conditional Probability (6.042J)

1:21:46
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Lecture 20: Independence (6.042J)

1:22:02
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Lecture 21: Random Variables (6.042J)

1:22:59
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Lecture 22: Expectation I (6.042J)

1:23:54

Lecture 22: Expectation I (6.042J)

Tom LeightonMIT

Professor

1:23:54·2012·undergraduate level

The concept of mathematical expectation is introduced, covering expected values of discrete random variables and linearity of expectation with practical examples.

Systems & CSTheory & AlgosMathematical ExpectationExpected ValueLinearity of ExpectationDiscrete Random Variables

Lecture 22: Expectation I

Instructor: Tom Leighton

Previous · Lecture 21

Lecture 21: Random Variables (6.042J)

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