02:58

The average minimum = The sum of powers

41:43

Stand-up Maths' max-of-dice conjecture finally proven!

27:17

Counterintuitive Coin Flips meet Deep Neural Network Theory

18:25

Putnam Probability Picture Proof

01:43:26

Probability's "Darth Vader Rule" with Manim Tutorial Live Coding

29:04

"A Random Variable is NOT Random and NOT a Variable"

08:02

Find and Fix Random Errors the Easy Way: Hamming(7,4)

02:06

3 New Random Variable Identities a la 3Blue1Brown's Probability Challenge

28:51

3Blue1Brown's Probability Challenge Solved!

10:00

Fibonacci Converts miles to km the Fun Way #SoMEpi

28:57

The Coin Flip Game that Stumped Twitter: Alice HH vs Bob HT

35:56

Business Math - Intro to the course [ LECTURE RECORDING ] MATH1030 - See playlist in description

30:15

The Math of "The Trillion Dollar Equation"

01:12:26

[Lecture] Monte Carlo evaluation and control: A Gridworld Example | Intro to Markov Chains and RL

01:05:42

[Lecture] Is it safe to differentiate under the integral? Lebesgue Dominated Convergence theorem

01:02:19

[ Lecture ] Intro to Monte Carlo methods in Reinforcement Learning | Intro to Markov Chains and RL

01:14:15

[ Lecture ] Almost Everywhere vs L1 convergence and an absolute summability theorem | Intro Analysis

01:06:16

[ Lecture ] L1 is complete and the monotone convergence theorem for integrals | Intro to Analysis

01:21:02

L1 vs "L"1, Null sets & functions, Almost Everywhere vs Norm Convergence | Intro to Analysis

01:19:24

Live coding the Gambler's Problem using Value Iteration | Intro to Markov Chains and Reinforcement L

01:11:13

Lebesgue Integrals 3: Absolute value of functions and series | Intro to Functional Analysis

01:19:00

The Bellman Equation and 1 Player PIG solved with Value Iteration | Intro to Markov Chains and RL

15:22

How far does a simple random walk go in n steps? E|X_n| = ?

01:09:39

Lebesgue Integral 2: Write the function as an infinite sum of step functions | Intro to Analysis

42:41

Markov Chains with actions & dice game PIG | Intro to Markov Chains and Reinforcement Learning

01:17:12

Lebesgue Integral 1: Step functions & Interval Countable Additivity | Intro to Functional Analysis

01:21:36

Cauchy Sequences, Complete and Banach Spaces | Intro to Functional Analysis

01:19:16

Creating Markov chains by enlarging the state space & Baby Bellman Eqn | Intro Markov Chains and RL

01:17:40

Closed/compact & closed ball is compact iff finite dimensional space | Intro to Functional Analysis

01:12:15

Solving probabilities and expected values for Markov Chains & the (baby) Bellman Eqn | Intro to RL

01:08:15

Pointwise vs L1 vs Linfinity convergence + Equivalence of norms on finite dimensional spaces | Lec 3

01:16:01

Two state Markov chain example and the steady state distribution | Intro to Markov Chains Lecture 3

01:13:43

Normed Vector Spaces and Function Spaces | Intro to Functional Analysis Lecture 2

01:18:43

Snakes+Ladders probability problem in spreadsheet and Python | Intro to Markov Chains Lec 2

01:16:23

Functions are just fancy vectors | Intro to Functional Analysis Lecture 1

01:14:40

What is Reinforcement Learning? Lecture with 4 Examples | Intro to Markov Chains and RL

21:05

The FAST trick to test if n is prime (with Python code) | AKS Primality Testing in poly(log n) time

13:28

The Fractals of Pascal's Triangle

01:07:52

Intro to Data Science Lecture 22 | letter2Vec (baby names version of word2vec)

01:13:01

Intro to Data Science Lecture 21 | MNIST Neural net Regularization, autoencoders, word2vec overview

01:08:31

Intro to Data Science Lecture 20 | MNIST in JAX: softmax, cross entropy loss, Multilayer perceptron

01:13:52

Intro to Data Science Lecture 19 | MNIST with JAX package, from linear regression to neural networks

52:33

Intro to Data Science Lecture 18 | Examples of Principle Component Analysis and Vector Embeddings

01:14:50

Intro to Data Science Lecture 17 | The magic of eigenvector/values and Principle Component Analysis

01:10:43

Intro to Data Science Lecture 16 | Lasso Regressions / L1 Regularization and shapes of Lp norms

01:14:15

Intro to Data Science Lecture 15 | Normalizing Variables in Ridge Regression and Goodharts Law

01:10:47

Intro to Data Science Lecture 14 | Shrinkage methods and Ridge Regression / L2 Regularization

01:12:42

Intro to Data Science Lecture 13 | Multiple hypothesis testing and Bootstraping

01:17:26

Intro to Data Science Lecture 11 | Quadratic discriminant analysis ROC curves and types of error

01:06:09

Intro to Data Science Lecture 12 | Counting parameters and Naive Bayes on the Titanic Dataset

01:09:13

Intro to Data Science Lecture 10 | Bayes Theorem for Coins and Classifiers Kernel Density Estimation

01:14:10

Intro to Data Science Lecture 9 | Multi-class classification, Gradient Descent, and Titanic Dataset

01:12:01

Intro to Data Science Lecture 8 | Cross Entropy Loss derivation

47:18

Intro to Data Science Lecture 7 | Classification and K-Nearest Neighbour examples on MNIST digits

01:21:05

Intro to Data Science Lecture 6 | Examples of Variable Selection and Some Practical Tips

01:05:28

Into To Data Science Lecture 5 | Multiple Linear Regression is Hard! Counterintuitive Coeffiecients

01:22:38

Intro to Data Science Lecture 4 | Train vs Test Error, Confidence Intervals and Meaning of Signicant

01:24:20

Intro to Data Science Lecture 3 | Vector programming in Python/NumPy and training vs test sets

01:19:56

Intro to Data Science Lecture 2 | Variance and Bias in Nearby Neighbour Averaging

02:23

Intro to Data Science | Obsidian website with Wikipedia-style pages