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Applied Probability / Stats for Computer Science, DS and ML
Diving in with code
Code env setup and Python crash course (18:48)
Getting started with code: Feel of data (11:57)
Foundations, data types and representing data (21:28)
Practical note: one-hot vector encoding (4:46)
Exploring data types in code (12:18)
Central tendency, mean, median, mode (19:33)
Section Review Tasks
Measures of Spread
Dispersion and spread in data, variance, standard deviation (9:35)
Dispersion exploration through code (11:02)
Section Review Tasks
Applications and Rules for Probability
Intro to uncertainty, probability intuition (12:14)
Simulating coin flips for probability (17:16)
Conditional probability; the most important concept in stats (21:58)
Applying conditional probability - Bayes rule (9:40)
Application of Bayes rule in real world - Spam detection (8:21)
Spam detection - implementation issues (10:08)
Section Review Tasks
Counting
Rules for counting (Mostly optional) (16:34)
Section Review Tasks
Random Variables - Rationale and Applications
Quantifying events - random variables (10:09)
Two random variables - joint probabilities (13:45)
Distributions - rationale and importance (18:28)
Discrete distributions through code (5:03)
Continuous distributions - probability densities (20:08)
Continuous distributions code (5:03)
Case study - sleep analysis, structure and code (18:15)
Section Review Tasks
Visualization in Intuition Building
Visualizing joint distributions - the road to ML success (13:03)
Dependence and variance of two random variables (11:13)
Section Review Tasks
Applications to the Real World
Expected values - decision making through probabilities (6:34)
Entropy - The most important application of expected values (18:46)
Applying entropy - coding decision trees for machine learning (26:55)
Foundations of Bayesian inference (11:40)
Bayesian inference code through PyMC3 (6:13)
Section Review Task
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Code env setup and Python crash course
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prob-cs-notebooks.zip
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prob-cs-paper-notes.zip
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