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MIT 6.042J: Mathematics for Computer Science →0/22
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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
9

Lecture 9: Communication Networks (6.042J)

1:23:25
10

Lecture 10: Graph Theory III (6.042J)

1:22:50
11

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
15

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 19: Conditional Probability (6.042J)

Tom LeightonMIT

Professor

1:21:46·2012·undergraduate level

The lecture explores conditional probability, examining Bayes' Theorem and its applications in updating probabilities based on new evidence and sequential events.

Theory & AlgosMathematicsProbability & Information TheoryConditional ProbabilityBayes' TheoremDependent Events

Lecture 19: Conditional Probability

Instructor: Tom Leighton

Previous · Lecture 18

Lecture 18: Probability Introduction (6.042J)

Next · Lecture 20

Lecture 20: Independence (6.042J)

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