課程基本資料
系所 / 年級Department/Grade-Level
通識選組 1年級
課號 / 班別Course Code
95U000730 / A
學分數Credits
2 學分
選 / 必修Elective / Required
通識(General)
科目中文名稱Course Title (Chinese)
人工智慧導論
科目英文名稱Course Title (English)
Artificial intelligence
負責教師Instructor
江玠峰(Chiang, Chieh-Feng)
開課期間Course Load
一學期
人數上限Enrollment Max.
50 人
已選人數Enrollment Taken
50 人
抽籤自動遞補等候人數Number of the waiting list after ballot
35 人
備註Memo
如初選滿額,以畢業班為優先,再依年級高低,最後依權重排序。
可選學制 (availability)
大學部
1年級 至6年級
二技部
不可選
碩士班
不可選
博士班
不可選

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教學綱要
課程概述Course Description
本課程運用OMO (online merge offline)教學法,將人工智慧產業應用內容導入於課堂中,培養學生人工智慧、深度學習、機器學習等重要觀念,並以做中學的方式實際了解理論與實務。
教學目標Course Objectives
本課程將與國際大廠Nvidia合作,將其線上Moocs (Massive Open Online Courses) 教學資源導入本學期課程中,課堂中教師輔導學生以實務操作的方式進行學習Nvidia線上教學資源,並介紹如何在Windows系統安裝Anaconda、TensorFlow與Keras,透過使用TensorFlow與Keras來了解深度學習與類神經網路;待誘發學生興趣後,再傳授人工智慧與深度學習的重要理論與演算法。 This course takes online-merge-offline methodology introducing business applications to develop students’ concepts of artificial intelligence, deep learning, and machine learning, with hands on practices. This course will cooperate with Nvidia, to merge its online courses into this traditional physical course. During the semester, students will learn how to install Anaconda, TensorFlow and Keras on windows systems. After activating students’ motivations on learning AI, classical algorithms will be introduced. Students will study and design the relevant applications with strong AI theory.
先修科目Prerequisites
None
教學方式Teaching Methods
評量方式Assessment
參考書目Reference
1. Artificial Intelligence: A guide to intelligent systems (2/e), Michael Negnevitsky, Addison Wesley, 2005. 2. Artificial Intelligence: A Modern Approach (2nd Ed.), Stuart Russell et al., Prentice Hall, 2003. 3. Nvidia Deep Learning Institute, https://www.nvidia.com/en-us/deep-learning-ai/education/. 4. “TensorFlow+Keras深度學習人工智慧實務應用”,林大貴,博碩,2017 5. “深度學習快速入門—使用 TensorFlow”, Giancarlo Zaccone,博碩,2017
教學進度Course Schedule
2020/10/09 人工智慧、機器學習、深度學習介紹 1. 人工智慧、機器學習與深度學習的關係 2. 機器學習介紹 3. 機器學習分類 4. 深度學習介紹 5. 總結與分析比較 Introduction to artificial intelligence, machine learning and deep learning 1. What is artificial intelligence 2. Introduction to machine learning 3. The types of machine learning algorithms 4. Introduction to deep learning 5. Summary 江玠峰(Chiang, Chieh-Feng)
2020/10/16 認識Nvidia提供之線上運算資源與環境 1. 深度學習(Deep Neural Network)介紹 2. 圖形化運算單元(GPU)介紹 3. 大數據(Big Data)介紹 4. 人工智慧運算環境介紹 Along with the course “Fundamentals of Deep Learning for Computer Vision” by Nvidia, students will learn implementing common deep learning workflows, such as image classification and object detection. 江玠峰(Chiang, Chieh-Feng)
2020/10/23 使用深度學習建立應用程式 1. 深度學習專案(Project)建立介紹 2. 深度學習環境佈署 Along with the course “Fundamentals of Deep Learning for Computer Vision” by Nvidia, students will learn experimenting with data, training parameters, network structure, and other strategies to increase performance and capability. 江玠峰(Chiang, Chieh-Feng)
2020/10/30 深度學習效能檢測與改進 1. 深度學習成效分類 2. 佈署預先訓練過的網路模組 3. 影像分類的概念 4. 實務操作 Along with the course “Fundamentals of Deep Learning for Computer Vision” by Nvidia, students will learn deploying neural networks to start solving real-world problems. 江玠峰(Chiang, Chieh-Feng)
2020/11/06 實務操作演練 1. 訓練深度類神經網路 2. 佈署深度類神經網路於Nvidia環境上 Along with the course “Fundamentals of Deep Learning for Computer Vision” by Nvidia, students will learn measuring and Improving Performance. 江玠峰(Chiang, Chieh-Feng)
2020/11/13 在Windows安裝TensorFlow與Keras 1. 安裝Anaconda教學 2. 命令提示字元操作環境介紹 3. 建立TensorFlow的Anaconda虛擬環境 4. 在Anaconda虛擬環境安裝TensorFlow與Keras 5. 啟動Jupyter Notebook Install Anaconda, TensorFlow and Keras on the windows system 1. Introduction to command prompt 2. Establish Anaconda's virtual environment 3. Install TensorFlow and Keras 4. Start Jupyter Notebook 江玠峰(Chiang, Chieh-Feng)
2020/11/20 Keras MNIST手寫數字辨識資料集介紹 1. 下載MNIST資料教學 2. 查看單筆訓練資料 3. 查看多筆訓練資料images與label 4. 多層感知器模型資料預處理 5. Features(特徵值)資料處理 6. Labels(正確值)資料處理 Introduction to the MNIST dataset 1. Download the MNIST data 2. Check the single training data 3. View the training data and the testing data 4. Data preprocessing 5. Feature Selection 6. Labels 江玠峰(Chiang, Chieh-Feng)
2020/11/27 Keras多元感知器(MLP)辨識手寫數字 1. Keras多元感知器(MLP)辨識Mnist手寫數字介紹 2. 進行資料預處理 3. 建立模型 4. 進行訓練 5. 以測試資料評估模型準確率 6. 進行預測 7. 隱藏層增加為1000個神經元 8. 總結與討論 Introduction to Multilayer perceptron (MLP) 1. Introduction to MLP Model 2. Data preprocessing 3. Modeling 4. Training the network using the training dataset 5. Predicted results 6. Selection of number of nodes in hidden layer 7. Discussion 江玠峰(Chiang, Chieh-Feng)
2020/12/04 實作測驗 Programming practice 江玠峰(Chiang, Chieh-Feng)
2020/12/11 影像分類 (Image Classification) 1. 載入影像資料進訓練環境當中 2. 選擇並訓練網路 3. 測試新資料並逐步提高效能與準確度 Image classification Train a deep neural network to recognize handwritten digits by: 1. Loading image data to a training environment 2. Choosing and training a network 3. Testing with new data and iterating to improve performance 江玠峰(Chiang, Chieh-Feng)
2020/12/18 物體偵測 (Object Detection) 1. 結合傳統電腦視覺與深度學習 2. 使用深度學習架構Caffe Object detection 1. Combining traditional computer vision with deep learning 2. Performing minor “brain surgery” on an existing neural network using the deep learning framework Caffe 3. Harnessing the knowledge of the deep learning community by identifying and using a purpose-built network and end-to-end labeled data 江玠峰(Chiang, Chieh-Feng)
2020/12/25 影像分割 (Image Segmentation) 1. 比較影像分割與其他電腦視覺的問題 2. 運用Tensorflow工具如TensorBoard 3. 學習實作有效能的測量工具 Image Segmentation Learn how to segment MRI images to measure parts of the heart by: 1. Comparing image segmentation with other computer vision problems 2. Experimenting with TensorFlow tools such as TensorBoard and the TensorFlow Python API 3. Learning to implement effective metrics for assessing model performance 江玠峰(Chiang, Chieh-Feng)
2021/01/01 訊號處理 (Signal Processing) 1. 將非影像資料視為影像資料來處理 2. 在Digits中實作一深度學習的流程 3. 提高效能與準確度 Signal processing 1. Treat non-image data as image data 2. Implement a deep learning workflow (load, train, test, adjust) in DIGITS 3. Test performance programmatically and guide performance improvements 江玠峰(Chiang, Chieh-Feng)
2021/01/08 古典人工智慧理論探討-Uninformed search 1. 樹與圖形的搜尋法 2. 廣度搜尋法、深度搜尋法 Uninformed search 1. Search on Tree and Graph 2. Breadth-First Search, Depth-First Search, Uniform-Cost Search 江玠峰(Chiang, Chieh-Feng)
2021/01/15 古典人工智慧理論探討-Informed search 1. 最佳搜尋法 2. 貪婪搜尋法 3. A* 搜尋法及其最佳化 Informed search 1. Best-First Search - Greedy Search, A* Search, Optimality of A* 2. Heuristic - Preformance, Generating Heuristics 江玠峰(Chiang, Chieh-Feng)
2021/01/22 古典人工智慧理論探討-Adversarial search 1. Alpha-Beta 刪除法 Adversarial search 1. Alpha-Beta Pruning 江玠峰(Chiang, Chieh-Feng)
2021/01/29 古典人工智慧理論探討-命題邏輯 1. 推論、分析(Resolution)、CNF(conjunctive normal form) 2. 前向連鎖 (forward chaining) 工作分析法 3. 倒向連鎖 (backward chaining) 工作分析法 Propositional Logic 1. Inference - Simple Knowledge, Resolution and CNF 2. Forward Chaining 3. Backward Chaining 江玠峰(Chiang, Chieh-Feng)
2021/02/05 期末考 Final exam & project report 江玠峰(Chiang, Chieh-Feng)