21:20 The Hermite Polynomials & Probability | Lecture at Canadian Math Society 2025 Prof Mihai Nica 950 views - 2 months ago
41:49 The Birthday Problem & Other Paradoxes: A Step-by-Step Guide #SoME4 Prof Mihai Nica 1.6K views - 5 months ago
10:59 How to calculate Best-of-7 win probability & bets on the final outcome of a series Prof Mihai Nica 2.1K views - 8 months ago
15:44 Why is Fibonacci hiding here? Random Walk on a pentagon Prof Mihai Nica 3.4K views - 9 months ago
6:29 Hank Green's 4x3 Word Connections & Venn Diagram Generalizations | Made with Marimo Prof Mihai Nica 324 views - 10 months ago
9:52 The probability proof that Pi is in the Normal Distribution #PiDay Prof Mihai Nica 1.7K views - 11 months ago
29:33 How does DeepSeek learn? GRPO explained with Triangle Creatures Prof Mihai Nica 22.7K views - 11 months ago
2:58 The average minimum = The sum of powers & Faulhaber's formula for the sum of powers Prof Mihai Nica 2.4K views - 1 year ago
41:43 The math of rolling with advantage: Stand-up Maths' max-of-dice conjecture proven! Prof Mihai Nica 25.3K views - 1 year ago
27:17 Counterintuitive Coin Flips meet Deep Neural Network Theory Prof Mihai Nica 2.1K views - 1 year ago
1:43:26 Probability's "Darth Vader Rule" with Manim Tutorial Live Coding Prof Mihai Nica 2.9K views - 1 year ago
2:06 3 New Random Variable Identities a la 3Blue1Brown's Probability Challenge Prof Mihai Nica 1.3K views - 1 year ago
28:57 The Coin Flip Game that Stumped Twitter: Alice HH vs Bob HT Prof Mihai Nica 93.4K views - 1 year ago
35:56 Business Math - Intro to the course [ LECTURE RECORDING ] MATH1030 - See playlist in description Prof Mihai Nica 1.2K views - 1 year ago
1:12:26 [Lecture] Monte Carlo evaluation and control: A Gridworld Example | Intro to Markov Chains and RL Prof Mihai Nica 1K views - 1 year ago
1:05:42 [Lecture] Is it safe to differentiate under the integral? Lebesgue Dominated Convergence theorem Prof Mihai Nica 739 views - 1 year ago
1:02:19 [ Lecture ] Intro to Monte Carlo methods in Reinforcement Learning | Intro to Markov Chains and RL Prof Mihai Nica 360 views - 1 year ago
1:14:15 [ Lecture ] Almost Everywhere vs L1 convergence and an absolute summability theorem | Intro Analysis Prof Mihai Nica 281 views - 2 years ago
1:06:16 [ Lecture ] L1 is complete and the monotone convergence theorem for integrals | Intro to Analysis Prof Mihai Nica 149 views - 2 years ago
1:21:02 L1 vs "L"1, Null sets & functions, Almost Everywhere vs Norm Convergence | Intro to Analysis Prof Mihai Nica 207 views - 2 years ago
1:19:24 Live coding the Gambler's Problem using Value Iteration | Intro to Markov Chains and Reinforcement L Prof Mihai Nica 590 views - 2 years ago
1:11:13 Lebesgue Integrals 3: Absolute value of functions and series | Intro to Functional Analysis Prof Mihai Nica 113 views - 2 years ago
1:19:00 The Bellman Equation and 1 Player PIG solved with Value Iteration | Intro to Markov Chains and RL Prof Mihai Nica 356 views - 2 years ago
15:22 How far does a simple random walk go in n steps? E|X_n| = ? Prof Mihai Nica 2.3K views - 2 years ago
1:09:39 Lebesgue Integral 2: Write the function as an infinite sum of step functions | Intro to Analysis Prof Mihai Nica 293 views - 2 years ago
42:41 Markov Chains with actions & dice game PIG | Intro to Markov Chains and Reinforcement Learning Prof Mihai Nica 361 views - 2 years ago
1:17:12 Lebesgue Integral 1: Step functions & Interval Countable Additivity | Intro to Functional Analysis Prof Mihai Nica 256 views - 2 years ago
1:21:36 Cauchy Sequences, Complete and Banach Spaces | Intro to Functional Analysis Prof Mihai Nica 253 views - 2 years ago
1:19:16 Creating Markov chains by enlarging the state space & Baby Bellman Eqn | Intro Markov Chains and RL Prof Mihai Nica 267 views - 2 years ago
1:17:40 Closed/compact & closed ball is compact iff finite dimensional space | Intro to Functional Analysis Prof Mihai Nica 369 views - 2 years ago
1:12:15 Solving probabilities and expected values for Markov Chains & the (baby) Bellman Eqn | Intro to RL Prof Mihai Nica 1.5K views - 2 years ago
1:08:15 Pointwise vs L1 vs Linfinity convergence + Equivalence of norms on finite dimensional spaces | Lec 3 Prof Mihai Nica 320 views - 2 years ago
1:16:01 Two state Markov chain example and the steady state distribution | Intro to Markov Chains Lecture 3 Prof Mihai Nica 1.4K views - 2 years ago
1:13:43 Normed Vector Spaces and Function Spaces | Intro to Functional Analysis Lecture 2 Prof Mihai Nica 440 views - 2 years ago
1:18:43 Snakes+Ladders probability problem in spreadsheet and Python | Intro to Markov Chains Lec 2 Prof Mihai Nica 717 views - 2 years ago
1:16:23 Functions are just fancy vectors | Intro to Functional Analysis Lecture 1 Prof Mihai Nica 1.2K views - 2 years ago
1:14:40 What is Reinforcement Learning? Lecture with 4 Examples | Intro to Markov Chains and RL Prof Mihai Nica 949 views - 2 years ago
21:05 The FAST trick to test if n is prime (with Python code) | AKS Primality Testing in poly(log n) time Prof Mihai Nica 4.1K views - 2 years ago
13:28 The fractal patterns of Pascal's Triangle modulo a prime Prof Mihai Nica 3.7K views - 2 years ago
1:07:52 Intro to Data Science Lecture 22 | letter2Vec (baby names version of word2vec) Prof Mihai Nica 141 views - 2 years ago
1:13:01 Intro to Data Science Lecture 21 | MNIST Neural net Regularization, autoencoders, word2vec overview Prof Mihai Nica 234 views - 2 years ago
1:08:31 Intro to Data Science Lecture 20 | MNIST in JAX: softmax, cross entropy loss, Multilayer perceptron Prof Mihai Nica 190 views - 2 years ago
1:13:52 Intro to Data Science Lecture 19 | MNIST with JAX package, from linear regression to neural networks Prof Mihai Nica 239 views - 2 years ago
52:33 Intro to Data Science Lecture 18 | Examples of Principle Component Analysis and Vector Embeddings Prof Mihai Nica 229 views - 2 years ago
1:14:50 Intro to Data Science Lecture 17 | The magic of eigenvector/values and Principle Component Analysis Prof Mihai Nica 304 views - 2 years ago
1:10:43 Intro to Data Science Lecture 16 | Lasso Regressions / L1 Regularization and shapes of Lp norms Prof Mihai Nica 222 views - 2 years ago
1:14:15 Intro to Data Science Lecture 15 | Normalizing Variables in Ridge Regression and Goodharts Law Prof Mihai Nica 98 views - 2 years ago
1:10:47 Intro to Data Science Lecture 14 | Shrinkage methods and Ridge Regression / L2 Regularization Prof Mihai Nica 138 views - 2 years ago
1:12:42 Intro to Data Science Lecture 13 | Multiple hypothesis testing and Bootstraping Prof Mihai Nica 197 views - 2 years ago
1:17:26 Intro to Data Science Lecture 11 | Quadratic discriminant analysis ROC curves and types of error Prof Mihai Nica 134 views - 2 years ago
1:06:09 Intro to Data Science Lecture 12 | Counting parameters and Naive Bayes on the Titanic Dataset Prof Mihai Nica 103 views - 2 years ago