Teaching of Ying Nian Wu

Book on Machine Learning 2024

The above book was written for STAT M231A and STAT 413 for the Fall quarter of 2024. The focus was on current machine learning methods based on deep neural networks. (tex)

An earlier book (tex)


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

Machine Learning for Beginners (undergraduate 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

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

Probability for Beginners (undergraduate level, 2024)

Part 0 slides
Part 1 slides
Part 2 slides
Part 3 slides
Part 4 slides
tex

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

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 (tex)
Statistical Mechanics (tex)
From Mechanics to Control (tex) engine gear muscle

A Note on Probability Theory (lecture note for STATS 200A tex) (outdated, please check videos above.)

A Note on Machine Learning Methods (lecture note for STATS M231B, updated March 2020 tex | slides ) (outdated, please check the videos above.)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


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