教學大綱表 (111學年度 第2學期)
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課程名稱
Course Title		 (中文) 類神經系統
(英文) Artificial Neural Systems And Laboratory 		開課單位
Departments		 電機工程研究所
課程代碼
Course No.		 E5770A
授課教師
Instructor		 許超雲
學分數
Credit		 3.0 必/選修
core required/optional		 選修 開課年級
Level		 研究所
先修科目或先備能力(Course Pre-requisites)：
課程概述與目標(Course Overview and Goals)： 建立基本機器 學習之基礎及未來應用及研究之能力
教科書(Textbook) 自編講義
參考教材(Reference)
課程大綱 Syllabus 學生學習目標
Learning Objectives		 單元學習活動
Learning Activities		 學習成效評量
Evaluation		 備註
Notes
序
No.		 單元主題
Unit topic		 內容綱要
Content summary
1 Neural network 簡介 1.神經網路歷史
2.神經網路數位模式: P erceptron model 		對神經網路的模式: 輸入之加權, 輸出及加權調整有概念  
2 Adline/madline model 及Widraw-Hoff Learning 1. 學習法則之基礎/理論課程授課
2 Tenserflow及keras入手 		1. basic learning algorithms
2. tensorflow 及 Keras 安裝入手		 第一個實驗之(a): 攝氏對華氏溫度之轉換 (a): 熟習 Anaconda  
3 Adline/madline及Backpagation Learning Rule 1)Widrow learning
2)Multi-layer Perceptron
3) Backpagation Learning Rule 		Neural network= architecture + learning rule 第一個實驗之(a):攝氏對華氏溫度之轉換: 程式試RUN及修改  
4 introduction to backprogation BP Architecture and learning Rule Architcture and learning rule  
5 Complexity of machine learning Optimizer and Loss function for machine learning The complexity of learning and related performance index 第一個實驗之(b)): 攝氏對華氏溫度之轉換以MLP+BP方式實現  
6 Introduction to Autoencoder(AE) 1.Autoencoder= Encoder + Decoder without Supervision
2. PCA Vs. Autoencoder
3. Laternal space 		AE' s adavantage and Bottleneck 實驗二以自編碼器(AE)實現MNIST資料庫手寫數字之資料降維  
7 Variational Autoencoder (VAE) 1) 4 disadvantages of AE
2) VariationalAE
3) Crossentropy Vs. KL Diveregence
4) Random generator 		The Improved learnig algorithm and Architecture of BP 模擬期中考  
8 AE. VAE. and PCA Difference between AE VAE and PCA Difference between AE VAE and PCA  
9 Midterm Midterm Midterm  
10 Convolutional Neural network 1. Simple sensor makes contribution to visual intellengence.
2. CNN　architecture
3. Examples . 		Architecture and learning rule of CNN 實驗三 以變分自編碼器(VAE)實現 MNIST資料降維分析與產生測試  
11 Feedback NN 1) RNN
2) LSTM T 		he architecture and Learning rule of RNN and LSTM Final Project proposal  
12 Support vector machine Margin/support vector Margin/support vector 實驗四 以LSTM自動生成音樂  
13 Generator and Adversarial Neural Network(GAN) architecture and learning rule of GAN architecture and learning rule of GAN  
14 Reinforcement learning 1.reinforcement learning
2. Q learning
3. TD Q learning
4. Deep Reinforcement Learning 		basic theorem and applications 實驗五: Q learning  
15 Transformer basic and architecture basic and architecture  
16 Restricted Boltzman Machine 1. RBM　architecture
2. Learning rule 		RBM learning rule and architecture  
17 Report on Final project Report final project project present  
18 Final exam Final exam Final exam  


教學要點概述：
1.自編教材 Handout by Instructor：
□ 1-1.簡報 Slids
□ 1-2.影音教材 Videos
□ 1-3.教具 Teaching Aids
□ 1-4.教科書 Textbook
□ 1-5.其他 Other
□ 2.自編評量工具/量表 Educational Assessment
□ 3.教科書作者提供 Textbook

成績考核 Performance Evaluation： 期末考：30%   期中考：30%   實驗：40%  

教學資源(Teaching Resources)：
□ 教材電子檔(Soft Copy of the Handout or the Textbook)
□ 課程網站(Website)
扣考規定：http://eboard.ttu.edu.tw/ttuwebpost/showcontent-news.php?id=504