The Hermite Polynomials & Probability | Lecture at Canadian Math Society 2025

a² + b² = c² has infinitely many solutions.

The best "Guess Who?" strategy (and how I proved it)

The Birthday Problem & Other Paradoxes: A Step-by-Step Guide #SoME4

How to calculate Best-of-7 win probability & bets on the final outcome of a series

How long until a Random Walk comes back?

Why is Fibonacci hiding here? Random Walk on a pentagon

Hank Green's 4x3 Word Connections & Venn Diagram Generalizations | Made with Marimo

The probability proof that Pi is in the Normal Distribution #PiDay

How does DeepSeek learn? GRPO explained with Triangle Creatures

The average minimum = The sum of powers & Faulhaber's formula for the sum of powers

The math of rolling with advantage: Stand-up Maths' max-of-dice conjecture proven!

Counterintuitive Coin Flips meet Deep Neural Network Theory

Putnam Probability Picture Proof

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

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

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

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

3Blue1Brown's Probability Challenge Solved!

Fibonacci Converts miles to km the Fun Way #SoMEpi

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

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

The Math of "The Trillion Dollar Equation"

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The fractal patterns of Pascal's Triangle modulo a prime

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

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

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

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

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

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

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

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

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

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

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

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