CS596 Machine Learning Lecture 13: Principal Component Analysis

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The mathematics, how-to, and applications of principal component analysis (PCA)
  1. Yang Xu
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CS596 Machine Learning 1:09:17

CS596 Machine Learning Lecture 13: Principal Component Analysis

CS596 Machine Learning: 0:09:06

CS596 Machine Learning: Supervised and unsupervised learning

Gradient descent: What 0:26:48

Gradient descent: What is gradient and gradient descent algorithm?

CS596 Machine Learning: 0:19:21

CS596 Machine Learning: Linear regression - Matrix representation and normal equation in matrix form

CS596 Machine Learning: 0:10:54

CS596 Machine Learning: Task definition for linear regression problem

CS596 Machine Learning: 0:08:52

CS596 Machine Learning: Cost function

Evaluation of Machine 1:10:34

Evaluation of Machine Learning Models

S596 Machine Learning: 0:08:44

S596 Machine Learning: Linear regression overview and example

Convolutional neural network 1:00:38

Convolutional neural network (part 2): ResNet, 1X1 Conv, Inception network, and object localization

CS596 Machine Learning: 0:03:53

CS596 Machine Learning: Assignment 2 tutorial

Deep neural network 1:03:07

Deep neural network (part 2): Regularization techniques

Convolutional neural network 1:00:17

Convolutional neural network (part 1): Convolutional operation basics

Linear regression (part 1:10:00

Linear regression (part 2)

Convolutional neural network 0:28:49

Convolutional neural network (part 3): Common ConvNet models

Review for final 0:26:54

Review for final exam

Generative models: GDA, 1:01:41

Generative models: GDA, Naive Bayesian, and Generative adversarial networks (GAN)

Word embeddings: Distributed 1:00:58

Word embeddings: Distributed representation and implementation (word2vec as an example)

Word embeddings: What 1:08:51

Word embeddings: What and how (word2vec as an example)

Clustering algorithms (part 1:02:09

Clustering algorithms (part 2): Mixture of Gaussians and EM method

Convolutional neural network 1:10:18

Convolutional neural network (part 1): Conv & pooling layers, complete ConvNet examples

Recurrent neural network 1:00:18

Recurrent neural network (part 1)

Deep neural network 1:07:15

Deep neural network (part 3): Batch norm

13 Linear models 0:31:02

13 Linear models

Deep contextualized representations 1:01:02

Deep contextualized representations (part 2): Transformer, GPT, and BERT