Teaching of Ying Nian Wu

In the videos below, the sound effect may be better without earphones.

Machine Learning (graduate level, 2023)

Lecture 1 video | Lecture 1 note | models
Lecture 2 video | Lecture 2 note | regression and classification
Lecture 3 video | Lecture 3 note | Least squares and logistic regression
Lecture 4 video | Lecture 4 note | gradient and Hessian
Lecture 5 video | Lecture 5 note | gradient descent, momentum
Lecture 6 video | Lecture 6 note | overfitting and regularization
Lecture 7 video | Lecture 7 note | tree, forest, and boosting
Lecture 8 video | Lecture 8 note | XGB and adaboost
Lecture 9 video | Lecture 9 note | kernel regression
Lecture 10 video | Lecture 10 note | multivariate Gaussian
Lecture 11 video | Lecture 11 note | Gaussian process
Lecture 12 video | Lecture 12 note | support vector machine
Lecture 13 video | Lecture 13 note | primal-dual optimization
Lecture 14 video | Lecture 14 note | neural networks
Lecture 15 video | Lecture 15 note | neural network from kernel perspective
Lecture 16 video | Lecture 16 note | back-propagation, Adam optimizer
Lecture 17 video | Lecture 17 note | CNN, RNN, LSTM
Lecture 18 video | Lecture 18 note | Transformer, GPT
Lecture 19 video | Lecture 19 note | GAN and VAE
Lecture 20 video | Lecture 20 note | AlphaGo, Reinforcement learning

Applied Probability (graduate level, 2022)

Lecture 1 video | Lecture 1 note | basic concepts
Lecture 2 video | Lecture 2 note | probability mass function, expectation, variance
Lecture 3 video | Lecture 3 note | Bernoulli, Binomial
Lecture 4 video | Lecture 4 note | Binomial, Geometric, probability density function
Lecture 5 video | Lecture 5 note | Uniform, Exponential, Poisson process
Lecture 6 video | Lecture 6 note | random walk, Brownian motion, Normal approximation
Lecture 7 video | Lecture 7 note | transformation, inversion method
Lecture 8 video | Lecture 8 note | joint distribution, Jacobian, polar method
Lecture 9 video | Lecture 9 note | multivariate statistics, correlation, regression
Lecture 10 video | Lecture 10 note | multivariate Normal, eigen decomposition
Lecture 11 video | Lecture 11 note | eigen analysis and multivariate calculus
Lecture 12 video | Lecture 12 note | conditioning, marginalization, factorization
Lecture 13 video | Lecture 13 note | conditional expectation, variance, covariance, conditional independence, causal analysis
Lecture 14 video | Lecture 14 note | Markov chain, conditional of multivariate Normal, Bayes rule
Lecture 15 video | Lecture 15 note | mixture model, acceptance-rejection sampling, factor analysis
Lecture 16 video | Lecture 16 note | Bayesian network, hidden Markov model, Kalman filtering, Markov decision process
Lecture 17 video | Lecture 17 note | Markov chain, eigen analysis, arrow of time, Page rank, Metropolis algorithm
Lecture 18 video | Lecture 18 note | Markov jump process, Brownian motion
Lecture 19 video | Lecture 19 note | generator, backward and forward equations, stochastic differential equation
Lecture 20 video | Lecture 20 note | law of large number, concentration inequality, central limit theorem, measure and integral

Machine Learning for Beginners (undergraduate and applied master level, 2022)

Lecture 1 video | Lecture 1 note | simple regression
Lecture 2 video | Lecture 2 note | linear regression
Lecture 3 video | Lecture 3 note | logistic regression
Lecture 4 video | Lecture 4 note | L2 and L1 regularization
Lecture 5 video | Lecture 5 note | trees
Lecture 6 video | Lecture 6 note | XGboost and adaboost
Lecture 7 video | Lecture 7 note | support vector machine (SVM)
Lecture 8 video | Lecture 8 note | primal-dual and kernelization
Lecture 9 video | Lecture 9 note | hinge loss
Lecture 10 video | Lecture 10 note | neural network
Lecture 11 video | Lecture 11 note | back-propagation
Lecture 12 video | Lecture 12 note | stochastic gradient descent
Lecture 13 video | Lecture 13 note | convolutional network
Lecture 14 video | Lecture 14 note | residual and recurrent networks
Lecture 15 video | Lecture 15 note | long short term memory (LSTM)
Lecture 16 video | Lecture 16 note | transformer
Lecture 17 video | Lecture 17 note | generative adversarial networks (GAN)
Lecture 18 video | Lecture 18 note | variational auto-encoder (VAE)
Lecture 19 video | Lecture 19 note | alphaGo
Lecture 20 video | Lecture 20 note | reinforcement learning

Probability for Beginners (undergraduate level, 2023)

Part 0 slides
Part 1 slides
Part 2 slides
Part 3 slides
Latex source

Lecture 1 video
Lecture 2 video
Lecture 3 video
Lecture 4 video
Lecture 5 video
Lecture 6 video
Lecture 7 video
Lecture 8 video
Lecture 9 video
Lecture 10 video
Lecture 11 video
Lecture 12 video
Lecture extra video

Monte Carlo for Beginners (undergraduate level, 2022)

Lecture 1 video | Lecture 1 note | Introduction
Lecture 2 video | Lecture 2 note | Random number generators
Lecture 3 video | Lecture 3 note | Inversion method
Lecture 4 video | Lecture 4 note | Transformation
Lecture 5 video | Lecture 5 note | Acceptance-rejection sampling
Lecture 6 video | Lecture 6 note | Particle swarm optimization and genetic algorithm
Lecture 7 video | Lecture 7 note | Monte Carlo integration
Lecture 8 video | Lecture 8 note | Importance sampling
Lecture 9 video | Lecture 9 note | Self-avoiding paths
Lecture 10 video | Lecture 10 note | Markov chain Monte Carlo
Lecture 11 video | Lecture 11 note | Metropolis algorithm
Lecture 12 video | Lecture 12 note | Gibbs sampler


Quantum Mechanics as RNN
Statistical Mechanics
From Mechanics to Control (tex) engine gear muscle

A Note on Probability Theory (lecture note for STATS 200A zip) (outdated, please check videos above.)

A Note on Machine Learning Methods (lecture note for STATS M231B, updated March 2020, zip | slides ) (outdated, please check the videos above.) neuron transistor

Books on Science