课程地址：https://www.cs.ubc.ca/~schmidtm/Courses/340-F19/

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。

目录如下：

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

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