02:19

Practical Uncertainty: Useful Ideas in Decision-Making, Risk, Randomness, & AI

09:25

Machine Learning Intro 7

10:15

Machine Learning Intro 6

10:43

Machine Learning Intro 5

07:37

Machine Learning Intro 4

10:40

Machine Learning Intro 3

12:50

Machine Learning Intro 2

21:49

Probabilistic Decision-Making Using Change-of-Probability Measures

11:41

Machine Learning Intro 1

04:33

Lecture 29: Stocks or Real Estate?

04:26

Lecture 28: Should I buy Extra Warranty?

03:41

Lecture 40: Exposing Ourselves to Positive High-Impact Events

03:11

Lecture 39: Increasing Our Chances of Success

02:53

Lecture 38: Unknown and Hidden Risks

05:45

Lecture 37: Efficiency and Its Flaws

05:18

Lecture 35: Potential Harm and Why the Rich Get Richer

02:56

Lecture 36: High Impact Rare Events

04:34

Lecture 34: Comparison Decisions, Halo Effects, and Quantifying

03:50

Lecture 33: Satisficing and Simplicity

04:18

Lecture 32: The Sunk Cost Fallacy

03:58

Lecture 31: How to Quantify and Prioritize?

04:20

Lecture 30: Using Expected Utility

02:56

Lecture 27: Risk Aversion and the Expected Values

02:41

Lecture 26: Potential Harm and Decision Structures

06:05

Lecture 25: Different Types of Decisions and Events

06:23

Lecture 24: Simplification and Decision Capacity

02:17

Lecture 23: Uncertainty, Confidence, and Rational Decisions

04:17

Lecture 22: Regression Toward the Mean

06:10

Lecture 21: Random Variables, Expectation, and the Law of Large Numbers

06:20

Lecture 20: How Should We Invest in the Stock Market?

03:23

Lecture 19: Introduction to Randomness in Finance

05:12

Lecture 18: Narrative Fallacy and Hindsight Bias

04:13

Lecture 17: Psychics' Strategies and Probability Amplification

04:53

Lecture 16: Birthdays and Related Scenarios in Life

04:59

Lecture 15: Psychics, Coincidences, and Bernoulli Trials

02:29

Lecture 14: Confirmation Bias and Lucky Shirts

06:29

Lecture 13: Courts and Bayes' Rule

06:06

Lecture 12: Bayes' Rule: False Positive Paradox and Cancer Screening

03:59

Lecture 11: Disease Probabilities

05:44

Lecture 10: Conditioning and Independence

03:57

Lecture 9: Probability Laws: Raining, Airplanes, and Elections

04:30

Lecture 8: Degree of Belief: Starting a Successful Business; Murder Trials

02:16

Lecture 7: Odds vs. Probability

06:59

Lecture 6: Finding Probabilities

05:12

Lecture 5: How to Interpret Statics; Correlation and Causation

06:29

Lecture 4: Probability, Relative Frequency, and Availability Heuristic

07:07

Lecture 3: Randomness and Thinking Flaws

04:26

Lecture 2: Thinking Modes

05:05

Lecture 1: Risk & Chance in Life & Business: Course Introduction

21:46

43 Laws of Iterated Expectations and Total Variance

08:19

42- Functions of Two Discrete Random Variables

10:45

41-Conditional Expectation and Law of Total Expectation

12:33

40- Conditional PMF and CDF

10:08

39- Joint CDF

12:44

38-Joint Probability Mass Function (PMF)

16:53

37 - Mixed Random Variables Using the Delta Function

13:37

36-Delta Function

19:58

35- Mixed Random Variables

25:20

33-Normal (Gaussian) Distribution

15:24

32-Exponential Random Variable