不可错过！《机器学习100讲》课程，UBC Mark Schmidt讲授

这是Mark Schmidt在UBC教机器学习的各种课程的课程材料的集合，包括100多个讲座的材料，涵盖了大量与机器学习相关的主题。

Part 1: Computer Science 340

1. Supervised Learning Overview

Exploratory Data Analysis

Decision Trees (Notes on Big-O Notation)

Fundamentals of Learning (Notation Guide)

Probabilistic Classifiers (Probability Slides, Notes on Probability)

Non-Parametric Models

Ensemble Methods

2. Unsupervised Learning Clustering

More Clustering

Outlier Detection

Finding Similar Items

3. Linear Models Least Squares (Notes on Calculus, Notes on Linear Algebra, Notes on Linear/Quadratic Gradients)

Nonlinear Regression

Gradient Descent

Robust Regression

Feature Selection

Regularization

More Regularization

Linear Classifiers

More Linear Classifiers

Feature Engineering

Convolutions

Kernel Methods

Stochastic Gradient

Boosting

MLE and MAP (Notes on Max and Argmax)

4. Latent-Factor Models Principal Component Analysis

More PCA

Sparse Matrix Factorization

Recommender Systems

Nonlinear Dimensionality Reduction

5. Deep Learning Deep Learning

More Deep Learning

Convolutional Neural Networks

More CNNs

Part 2: Data Science 573 and 575

Structure Learning

Sequence Mining

Tensor Basics

Semi-Supervised Learning

PageRank

Part 3: Computer Science 440

A. Binary Random Variables Binary Density Estimation

Bernoulli Distribution

MAP Estimation

Generative Classifiers

Discriminative Classifiers

Neural Networks

Double Descent Curves

Automatic Differentiation

Convolutional Neural Networks

Autoencoders

Fully-Convolutional Networks

B. Categorical Random Variables Monte Carlo Approximation

Conjugate Priors

Bayesian Learning

Empirical Bayes

Multi-Class Classification

What do we learn?

Recurrent Neural Networks

Long Short Term Memory

Attention and Transformers

C. Gaussian Random Variables Univariate Gaussian

Multivariate Gaussian (Motivation)

Multivairate Gaussian (Definition)

Learning Gaussians

Bayesian Linear Regression

End to End Learning

Exponential Family

D. Markov Models Markov Chains

Learning Markov Chains

Message Passing

Markov Chain Monte Carlo

Directed Acyclic Graphical Models

Learning Graphical Models

Log-Linear Models

E. Latent-Variable Models Mixture Models

EM and KDE (Notes on EM)

HMMs and RBMs (Forward-Backward for HMMs)

Topic Models and Variational Inference

VAEs and GANs