WEEK 1 - 09/08

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

WEEK 2 - 09/15

TopicLinear Model Classification: Probabilistic Generative Model, Logistic Regression

WEEK 3 - 09/22

TopicNeural Networks: Introduction, Gradient Decent and Back Propagation, Tips, Implementation

WEEK 5 - 10/06

Topic1. Dimensionality Reduction: Principle Component Analysis
    2. Convolutional Neural Network (CNN) (李宏毅教授教學影片)

WEEK 6 - 10/13

TopicAuto Encoder

WEEK 7 - 10/20

TopicNeighbor Embedding

WEEK 8 - 10/27

Topic1. Ensemble: Random forest, AdaBoost
    2. Recurrent Neural Network (李宏毅教授教學影片)

WEEK 9 - 11/03

TopicExpectation Maximization and Gaussian Mixture Models

WEEK 10 - 11/10

TopicSemi-Supervised Learning

WEEK 11 - 11/17

TopicVariational Auto-Encoder

WEEK 12 - 11/24

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

WEEK 13 - 12/01

TopicStrong Duality Theorem

WEEK 14 - 12/08

TopicSupport Vector Machine: Kernel form and KKT conditions

WEEK 15 - 12/15

TopicProbably Approximately Correct Learning

HOMEWORK

PROGRAM