教學大綱表 (112學年度 第2學期)
請遵守智慧財產權,勿使用非法影印教科書,避免觸法。
課程名稱
Course Title
(中文) 人工智慧程式設計與Python
(英文) Ai Programming With Python
開課單位
Departments
資訊工程學系
課程代碼
Course No.
I3960
授課教師
Instructor
葉慶隆
學分數
Credit
3.0 必/選修
core required/optional
選修 開課年級
Level
大三
先修科目或先備能力(Course Pre-requisites):微積分、線性代數、機率統計、物件導向程式設計、資料庫系統
課程概述與目標(Course Overview and Goals): 學習Python程式設計及用來建構類神經網路所需的基礎模組,包括NumPy、pandas、Matplotlib、PyTorch、Calculus、及Linear Algebra。類神經網路架構概念介紹,包括淺層、深層網路。tensorflow/kera工具操作應用練習。
以及人工智慧應用包括電腦視覺、及自然語言諸應用案例。此外也練習以python api管理語意網/連結資料(linked data)。
教科書(Textbook) Python for Everybody: Exploring Data in Python 3
參考教材(Reference) AI Programming with Python, Udacity (MOOCS 課程)
CS50's Introduction to Artificial Intelligence with Python, Harvard University
Python Tutorial, w3schools.com
NumPy Tutorial, w3schools.com
Pandas Tutorial, w3schools.com
Matplotlib Tutorial, w3schools.com
Machine Learning, w3schools.com
SymPy: a Python library for symbolic mathematics
Learn pandas by Hernan Roja
Python Deep Learning Tutorial, Tutorialspoint
AI with Python – Neural Networks, Tutorialspoint
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

No.
單元主題
Unit topic
內容綱要
Content summary
1 Python Programming (1) 1. Background
2. Variables, expressions, and statements
3. Conditional execution
4. Anaconda, Jupyter Notebook, google colab
1. Programming background
2. Variables, expressions, and statements
3. Conditional execution
4. Anaconda, Jupyter Notebook, google colab
 
2 Python Programming (2) 1. Functions
2. Iteration
3. Strings
1. Functions
2. Iteration
3. Strings
 
3 Python Programming (3) 1. Files
2. Lists
3. Dictionaries
1. Files
2. Lists
3. Dictionaries
 
4 Python Programming (4) 1. Tuples
2. Regular Expressions
1. Tuples
2. Regular Expressions
 
5 NumPy 1. NumPy 1. NumPy  
6 NumPy, Pandas 1. Numpy
2. Pandas
1. Numpy
2. Pandas
 
7 Pandas, Matplotlib 1. Pandas
2. Matplotlib
1. Pandas
2. Matplotlib
 
8 Building A Logistic Regression in Python (1) 1. Introduction to Logistic Regression
2. Problem Description and Datasets
3. Data exploration
4. Visualizations
Building A Logistic Regression in Python
1. Introduction to Logistic Regression
2. Problem Description and Datasets
3. Data exploration
4. Visualizations
 
9 期中考週 期中考試 期中考試  
10 Building A Logistic Regression in Python (2) 5. Create dummy variables
6. Over-sampling using SMOTE
7. Recursive Feature Elimination
8. Implementing the model
5. Create dummy variables
6. Over-sampling using SMOTE
7. Recursive Feature Elimination
8. Implementing the model
 


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:

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