# 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, 2022)

**Lecture 1 video |
Lecture 1 note** | Proportion and frequency

**Lecture 2 video |
Lecture 2 note** | Random variable, expectation and variance

**Lecture 3 video |
Lecture 3 note** | Bernoulli and Binomial

**Lecture 4 video |
Lecture 4 note** | Expectation and variance of Binomial

**Lecture 5 video |
Lecture 5 note** | Galton board and random walk

**Lecture 6 video |
Lecture 6 note** | Probability density

**Lecture 7 video |
Lecture 7 note** | Uniform and Exponential

**Lecture 8 video |
Lecture 8 note** | Poisson process and Normal

**Lecture 9 video |
Lecture 9 note** | Normal and diffusion

**Lecture 10 video |
Lecture 10 note** | Joint, marginal and conditional

**Lecture 11 video |
Lecture 11 note** | Bayes and Markov

**Lecture 12 video |
Lecture 12 note** | Correlation, regression and limiting theorems

## 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

**A Note on Probability Theory ** (lecture note for STATS 200A zip)
Undergraduate version:
Part 1
Part 2
Part 3 (zip) (a bit outdated, please check videos above.)

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slides ) (a bit outdated, please check the videos above.)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

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

**Books on Science**