I4850 教學大綱表

教學大綱表 (112學年度第1學期)

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課程名稱 Course Title	(中文) 類神經網路概論 (英文) Introduction To Neural Networks			開課單位 Departments	資訊工程學系
				課程代碼 Course No.	I4850
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	大四
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研究之能力
教科書(Textbook)	自編講義
參考教材(Reference)	每個課題之講義均有列參考教材

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	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
1	Neural network 簡介及應用:	1. 機器學習, 神經網路之沿起歷史. 2. 機器學習之重要運用	了解類神經系統重要			從機器學習到深度學習的介紹
2	Adline/madline model 及Widraw-Hoff Learning	基礎/理論課程授課及Tenserflow及keras入手	1. basic learning algorithms 2. tensorflow 及 Keras 安裝入手			第一個實驗之(a): 華氏對攝氏溫度之轉換 (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): 華氏對攝氏溫度之轉換: 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)): 華氏對攝氏溫度之轉換以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	Improve learnig algorithm and Architecture of BP			模擬期中考
8	Midterm	Midterm	Midterm
9	AE. VAE. PCA	Difference between AE VAE and PCA	Difference between AE VAE and PCA
10	Convolutional Neural network	1. Simple sensor makes contribution to visual intellengence. 2. CNN　architecture 3. Examples demo.	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	The 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
15	Transformer	basic and architecture	basic and architecture
16	Final exam	final exam	final exam
17	彈性教學 Final project competion	Final project competion	Final project competion			彈性教學
18	彈性教學:Reinforcement Learning Project	熟習 open-AI Gym	建立Flappy Bird遊戲			彈性教學

彈性教學週活動規劃

序 No.	實施期間 Period	實施方式 Content	教學說明 Teaching instructions	彈性教學評量方式 Evaluation	備註 Notes
1	起:2024-01-01 迄:2024-01-12	5.小專題 Project	Applications of LLM or TrAnsformer	創意及完整度

教學要點概述：
教材編選(Teaching Materials)：
■ 1-1.簡報 Slids
■ 1-2.影音教材 Videos
□ 1-3.教具 Teaching Aids
□ 1-4.教科書 Textbook Slids
■ 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：30% 彈性教學：10%

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