課程名稱 (中文) 類神經系統及實驗 (英文) Artificial Neural Systems And Laboratory 開課單位 電機工程研究所 課程代碼 E5770A 授課教師 許超雲 學分數 3.0 必/選修 選修 開課年級 研究所 先修科目或先備能力： 課程概述與目標： 建立基本機器 學習之基礎及未來研究之能力 教科書 參考教材
 課程大綱 學生學習目標 單元學習活動 學習成效評量 備註 週 單元主題 內容綱要 1 Neural network 簡介: 歷史及Perceptron model 了解類神經系統定義基礎 講授閱讀討論 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 入手 心得發表講授上機實習 實驗 3 Adline/madline Widrow learning LMS learning 實作講授 4 introduction to backprogation BP Architecture and learning Rule Architcture and learning rule 講授 實驗 5 concave and convex for machine learning concave and convex for machine learning The complexity of learning 講授 6 Discusion of Project 1 and 2 discusion of project 1: random number generator Project 2: Percetron Learning Coding Algorithm 個案研究心得發表講授 實驗 7 Improve the BP Improve learnig algorithm and Architecture of BP Improve learnig algorithm and Architecture of BP 8 Radical Basis Fuction Network Architecture of RBF and Learning Rule Architecture of RBF and Learning Rule 講授 9 Midterm Midterm Midterm 講授 期中考 10 Self-organizing Feature map Architecture and learning Rule for SOFM Architecture and learning Rule for SOFM 講授 11 Gradient and discrete Hofield Model The architecture and Learning rule of Hopfield model The architecture and Learning rule of Hopfield model 講授 實驗 12 Boltzman Machine Architecture and learning rule of BM Architecture and learning rule of BM 講授 13 Support vector machine (I) Margin/support vector 1) Margin/support vector 講授 14 Support Vector machine (2) SVM architecture and learning rule SVM architecture and learning rule 15 reinforcement learning reinforcement learning basic theorem and applications 講授 16 Adaptive resonant T(ART) Plasticity and stabaility ART 1 期末考 17 Final exam final exam final exam 期末考
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