深度學習與人機互動技術
Deep Learning and Human Interface
學期
109-1
學分
3
學分
當期課號
5966
永久課號
IOG5023
開課單位
智慧科學暨綠能學院
授課教師
謝君偉
校區
歸仁
類別
選修
上課時間表
| 節 | 週二 |
|---|---|
5 13:20–14:10 | 深度學習與人機互動技術 CM214 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
概述
The contents of this course discusses the basic concepts and architecture of deep learning and its several vision-based applications including: similarity measure, backpropagation neural networks, convolutional neural networks, recurrent neural networks, feature extraction, face recognition, pedestrian detection, vehicle detection, and so on.
先修科目
Linear Algebra
教學方式
Class lecture, presentation with slides
評分方式
Homework 20%,General Exam 20%, Mid Exam 30%, Final Exam 30%
週次計畫
| 週次 | 主題 |
|---|---|
| 第 1 週 | Chapter 1: Introduction |
| 第 2 週 | Chapter 2: Applied Math |
| 第 3 週 | Chapter 3: Probability and Information Theory |
| 第 4 週 | Chapter 4: Machine Learning Basics |
| 第 5 週 | Chapter 5: Unsupervised and Supervised Learning |
| 第 6 週 | Chapter 6: Stochastic Gradient Descent |
| 第 7 週 | Chapter 7: Backpropagation Learning |
| 第 8 週 | Chapter 8: Regularization and Optimization for Deep Learning |
| 第 9 週 | General Exam |
| 第 10 週 | Mid Exam |
| 第 11 週 | Chapter 9: Deep Convolution Neural Networks |
| 第 12 週 | Chapter 10: Sequence Modeling: Recurrent and Recursive Nets |
| 第 13 週 | Chapter 11: Face Recognition |
| 第 14 週 | Chapter 12: Pedestrian Detection |
| 第 15 週 | Chapter 13: Vehicle Analysis |
| 第 16 週 | Chapter 13: Vehicle Analysis |
| 第 17 週 | overall review |
| 第 18 週 | Final Exam |
教科書
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville Machine Learning: an algorithm perspective by S. Marsland