Courses | 機器學習與估計理論實驗室

WEEK 1 - 09/06

Topic 1. Introduction;
2. Regression; Bias and Variance Errors

Slide	Video
PPT EE5184 Syllabus PPT Introduction PPT Regression	VIDEO Introduction, Regression

Slide

Video

PPT EE5184 Syllabus

PPT Introduction

PPT Regression

VIDEO Introduction, Regression

WEEK 2 - 09/13

TopicLinear Model Classification: Probabilistic Generative Model, Logistic Regression

Slide	Video
PPT Bias and Variance PPT Classification - Probability Generative Model PPT Logistic Regression	VIDEO Linear Model Classification

Slide

Video

PPT Bias and Variance

PPT Classification - Probability Generative Model

PPT Logistic Regression

VIDEO Linear Model Classification

WEEK 3 - 09/20

Topic1. Ensemble: Random forest, AdaBoost
2. Neural Networks: Introduction, Gradient Decent and Back Propagation, Tips, Implementation (李宏毅教授教學影片)

Slide	Video
PPT Backpropagation PPT Deep Learning PPT Tips for Deep Learning PPT Gradient Descent	VIDEO Ensemble VIDEO Deep Learning (last year)

Slide

Video

PPT Backpropagation

PPT Deep Learning

PPT Tips for Deep Learning

PPT Gradient Descent

VIDEO Ensemble

VIDEO Deep Learning (last year)

WEEK 4 - 09/27

Topic1. Dimensionality Reduction: Principle Component Analysis
2. Auto encoder (李宏毅教授教學影片)

Slide	Video
PPT Ensemble PPT Adaboost PPT Auto Encoder	VIDEO Dimensionality Reduction VIDEO Ensemble and Boosting (last year)

Slide

Video

PPT Ensemble

PPT Adaboost

PPT Auto Encoder

VIDEO Dimensionality Reduction

VIDEO Ensemble and Boosting (last year)

WEEK 5 - 10/04

Topic1. Neighbor Embedding
2. Convolutional Neural Network (李宏毅教授教學影片)

Slide	Video
PPT CNN PPT PCA PPT PCA & SVD	VIDEO Neighbor Embedding VIDEO CNN

Slide

Video

PPT CNN

PPT PCA

PPT PCA & SVD

VIDEO Neighbor Embedding

VIDEO CNN

WEEK 6 - 10/11

Topic1. Expectation Maximization and Gaussian Mixture Models
2. Semi-Supervised Learning (李宏毅教授教學影片)

Slide	Video
PPT GMM & EM	VIDEO EM & GMM VIDEO Semi-supervised (2022)

Slide

Video

PPT GMM & EM

VIDEO EM & GMM

VIDEO Semi-supervised (2022)

WEEK 7 - 10/18

Topic1. Variational Auto-Encoder
2. Recurrent Neural Network (李宏毅教授教學影片)

Slide	Video
PPT VAE	VIDEO VAE

WEEK 8 - 10/25

Topic1. Support Vector Machine: Margin and primal form
2. Duality Theory of Constrained Optimization – Introduction

Slide	Video
PPT Neighbor Embedding PPT RNN	VIDEO SVM VIDEO RNN - 1 VIDEO RNN - 2

Slide

Video

PPT Neighbor Embedding

PPT RNN

VIDEO SVM

VIDEO RNN - 1

VIDEO RNN - 2

WEEK 9 - 11/01

TopicStrong Duality Theorem

Slide	Video
	VIDEO Strong Duality Theorem

WEEK 10 - 11/08

TopicSupport Vector Machine: Kernel form and KKT conditions

Slide	Video
PPT SVM Introduction PPT Optimization PPT Kernel PPT Duality Introduction PPT Duality	VIDEO SVM VIDEO SVM（僅黑板）

Slide

Video

PPT SVM Introduction

PPT Optimization

PPT Kernel

PPT Duality Introduction

PPT Duality

VIDEO SVM

VIDEO SVM（僅黑板）

WEEK 13 - 11/29

TopicReinforcement Learning: Markov Decision Process and Bellman optimality equations

Slide	Video
PPT RL Notes PPT Regret Bound	VIDEO Reinforcement Learning - 1 VIDEO Reinforcement Learning - 1 (僅黑版)

Slide

Video

PPT RL Notes

PPT Regret Bound

VIDEO Reinforcement Learning - 1

VIDEO Reinforcement Learning - 1 (僅黑版)

WEEK 14 - 12/06

TopicReinforcement Learning: Value and Policy Iterations; Multi-arm bandit problem

Slide	Video
	VIDEO Reinforcement Learning - 2 VIDEO Reinforcement Learning - 2 (僅黑版)

Slide

Video

VIDEO Reinforcement Learning - 2

VIDEO Reinforcement Learning - 2 (僅黑版)

WEEK 15 - 12/13

TopicProbably Approximately Correct Learning

Slide	Video
PPT PAC Introduction	VIDEO PAC VIDEO PAC (僅黑版)

Slide

Video

PPT PAC Introduction

VIDEO PAC

VIDEO PAC (僅黑版)

HOMEWORK

Name	Question	Answer
Math Problem 1 Math Problem 2 Math Problem 3 Math Problem 4 Math Problem 5	PDF Regression PDF CNN PDF Embedding PDF RNN PDF SVM	Not Available Not Available Not Available Not Available Not Available

Name

Question

Answer

Math Problem 1

Math Problem 2

Math Problem 3

Math Problem 4

Math Problem 5

PDF Regression

PDF CNN

PDF Embedding

PDF RNN

PDF SVM

Not Available

PROGRAM

Name	Slide / Video	Code
Program 1 Program 2 Program 3 Program 4 Program 5	PPT PM2.5 Prediction PPT Face Expression Prediction PPT AutoEncoder PPT Prediction of Postpartum Depression PPT Knight and Pawn	FILE Program1 Code FILE Program2 Code FILE Program3 Code FILE Program4 Code FILE Program5 Code

Name

Slide / Video

Code

Program 1

Program 2

Program 3

Program 4

Program 5

PPT PM2.5 Prediction

PPT Face Expression Prediction

PPT AutoEncoder

PPT Prediction of Postpartum Depression

PPT Knight and Pawn

FILE Program1 Code

FILE Program2 Code

FILE Program3 Code

FILE Program4 Code

FILE Program5 Code

MACHINE LEARNING

113-1 / Fall 2024

WEEK 1 - 09/06

Topic 1. Introduction;
2. Regression; Bias and Variance Errors

WEEK 2 - 09/13

TopicLinear Model Classification: Probabilistic Generative Model, Logistic Regression

WEEK 3 - 09/20

Topic1. Ensemble: Random forest, AdaBoost
2. Neural Networks: Introduction, Gradient Decent and Back Propagation, Tips, Implementation (李宏毅教授教學影片)

WEEK 4 - 09/27

Topic1. Dimensionality Reduction: Principle Component Analysis
2. Auto encoder (李宏毅教授教學影片)

WEEK 5 - 10/04

Topic1. Neighbor Embedding
2. Convolutional Neural Network (李宏毅教授教學影片)

WEEK 6 - 10/11

Topic1. Expectation Maximization and Gaussian Mixture Models
2. Semi-Supervised Learning (李宏毅教授教學影片)

WEEK 7 - 10/18

Topic1. Variational Auto-Encoder
2. Recurrent Neural Network (李宏毅教授教學影片)

WEEK 8 - 10/25

Topic1. Support Vector Machine: Margin and primal form
2. Duality Theory of Constrained Optimization – Introduction

WEEK 9 - 11/01

TopicStrong Duality Theorem

WEEK 10 - 11/08

TopicSupport Vector Machine: Kernel form and KKT conditions

WEEK 13 - 11/29

TopicReinforcement Learning: Markov Decision Process and Bellman optimality equations

WEEK 14 - 12/06

TopicReinforcement Learning: Value and Policy Iterations; Multi-arm bandit problem

WEEK 15 - 12/13

TopicProbably Approximately Correct Learning

EXERCISE

HOMEWORK

PROGRAM

MACHINE LEARNING

113-1 / Fall 2024

WEEK 1 - 09/06

Topic 1. Introduction; 2. Regression; Bias and Variance Errors

WEEK 2 - 09/13

TopicLinear Model Classification: Probabilistic Generative Model, Logistic Regression

WEEK 3 - 09/20

Topic1. Ensemble: Random forest, AdaBoost 2. Neural Networks: Introduction, Gradient Decent and Back Propagation, Tips, Implementation (李宏毅教授教學影片)

WEEK 4 - 09/27

Topic1. Dimensionality Reduction: Principle Component Analysis 2. Auto encoder (李宏毅教授教學影片)

WEEK 5 - 10/04

Topic1. Neighbor Embedding 2. Convolutional Neural Network (李宏毅教授教學影片)

WEEK 6 - 10/11

Topic1. Expectation Maximization and Gaussian Mixture Models 2. Semi-Supervised Learning (李宏毅教授教學影片)

WEEK 7 - 10/18

Topic1. Variational Auto-Encoder 2. Recurrent Neural Network (李宏毅教授教學影片)

WEEK 8 - 10/25

Topic1. Support Vector Machine: Margin and primal form 2. Duality Theory of Constrained Optimization – Introduction

WEEK 9 - 11/01

TopicStrong Duality Theorem

WEEK 10 - 11/08

TopicSupport Vector Machine: Kernel form and KKT conditions

WEEK 13 - 11/29

TopicReinforcement Learning: Markov Decision Process and Bellman optimality equations

WEEK 14 - 12/06

TopicReinforcement Learning: Value and Policy Iterations; Multi-arm bandit problem

WEEK 15 - 12/13

TopicProbably Approximately Correct Learning

EXERCISE

HOMEWORK

PROGRAM

Topic 1. Introduction;
2. Regression; Bias and Variance Errors

Topic1. Ensemble: Random forest, AdaBoost
2. Neural Networks: Introduction, Gradient Decent and Back Propagation, Tips, Implementation (李宏毅教授教學影片)

Topic1. Dimensionality Reduction: Principle Component Analysis
2. Auto encoder (李宏毅教授教學影片)

Topic1. Neighbor Embedding
2. Convolutional Neural Network (李宏毅教授教學影片)

Topic1. Expectation Maximization and Gaussian Mixture Models
2. Semi-Supervised Learning (李宏毅教授教學影片)

Topic1. Variational Auto-Encoder
2. Recurrent Neural Network (李宏毅教授教學影片)

Topic1. Support Vector Machine: Margin and primal form
2. Duality Theory of Constrained Optimization – Introduction