WEEK 2 - 09/16

Topic Introduction; Regression; Bias and Variance Errors

WEEK 3 - 09/23

TopicLinear Model Classification: Probabilistic Generative Model, Logistic Regression

WEEK 4 - 09/30

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

WEEK 5 - 10/07

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

WEEK 6 - 10/14

TopicAuto encoder

WEEK 7 - 10/21

TopicNeighbor Embedding

WEEK 8 - 10/28

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

WEEK 9 - 11/4

TopicExpectation Maximization and Gaussian Mixture Models

WEEK 10 - 11/11

TopicSemi-Supervised Learning

WEEK 11 - 11/18

TopicVariational Auto-Encoder

WEEK 12 - 11/25

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

WEEK 13 - 12/02

Topic1. Strong Duality Theorem
    2. Support Vector Machine: Kernel form and KKT conditions

WEEK 14 - 12/09

TopicProbably Approximately Correct Learning

WEEK 15 - 12/16

TopicPrivacy Preserving Machine Learning

HOMEWORK

PROGRAM