流水號
55704
課號
IMPS1004
課程識別碼
H41 10040
- 班次 02
- 3 學分
A6
無授課對象
無授課對象
A6- 陳彥賓
- 搜尋教師開設的課程
共同教育中心 統計碩士學位學程
yanbin@ntu.edu.tw
- Room 212, 2F, Chee-Chun Leung Cosmology Hall (次震宇宙館, 2樓, 212室)
02-33664688
個人網站
https://sites.google.com/view/yan-bin/home
- Yan-Bin Chen, PhD Assistant Professor Dept: Master Program in Statistics, Center for General Education
- 二 A, B, C
普304
2 類
修課總人數 40 人
本校 40 人
無領域專長
- 英文授課
- NTU COOL
- 備註
本課程以英語授課。限非電資學院學生修習。。A6:數學數位與量化分析
本校選課狀況
已選上0/40外系已選上0/0剩餘名額0已登記0- 課程概述The course is a practical programming class focused on artificial intelligence (AI). It aims to teach students introductory AI concepts and enable them to develop simple AI applications using Python. Specifically designed for non-EECS (Electrical Engineering and Computer Science) college beginners, the course covers the basic to advanced concepts of the Python programming language. The examples and exercises provided in the course primarily emphasize AI applications. Additionally, the course introduces a few contemporary AI applications. The course is taught in English, but bilingualism is acceptable for discussions and Q&A sessions. Teaching methods in each week: 50 mins: Lecture on the programming skill. 80 mins: Students engage in hands-on exercises and teamwork. 20 mins: Lecture on the fundamental knowledge. If you would like to take the course but were unable to successfully enroll, please come to class in the first week. However, if the numer of extra enrollment students is larger than five, we may select five from them to obtain the authrization codes.
- 課程目標At the beginning of this class, students are expected to have hands-on programming experience in the Python language. By the end of the curriculum, they will be able to showcase their artificial intelligence programs through their final projects.
- 課程要求The students should take along with their laptops in the class session.
- 預期每週課前或/與課後學習時數4 hours
- Office Hour
*此 Office Hour 需要提前預約 - 指定閱讀Month 1,2: Book 1 Chapter 2,3,4,5 Month 2,3: Book 1 Chapter 6,7,8,9 Month 3,4: Book 2 Chapter 1,2,4
- 參考書目Book 1: Python for Data Analysis, 3E --- Data Wrangling with Pandas, NumPy, and Jupyter, 2022 By Wes McKinney Book 2: Artificial Intelligence with Python, 2017 By Prateek Joshi Online reading: Python Tutorial website. (https://www.tutorialspoint.com/python/)
- 評量方式
50% In class: exercise in class session
50% Final: final project (peer evaluation 10%)
- 本校尚無訂定 A+ 比例上限。
- 本校採用等第制評定成績,學生成績評量辦法中的百分制分數區間與單科成績對照表僅供參考,授課教師可依等第定義調整分數區間。詳見 學習評量專區。
- 針對學生困難提供學生調整方式
調整方式 說明 A3 提供學生彈性出席課程方式
Provide students with flexible ways of attending courses
B5 團體報告取代個人報告
Group report replace Personal report
B6 學生與授課老師協議改以其他形式呈現
Mutual agreement to present in other ways between students and instructors
C2 書面(口頭)報告取代考試
Written (oral) reports replace exams
D1 由師生雙方議定
Negotiated by both teachers and students
- 補課資訊
- 課程進度
第 1 週 Introduction 第 2 週 [Part 1: Basic Python for Beginners] Introduction to Python & Environment Setup (Chap 2) 第 3 週 Python Syntax and Data Structure (Chap 3) 第 4 週 Array and Vectorized Computations for Common Data Processing Tasks (Chap 4) 第 5 週 Pandas (Chap 5) 第 6 週 Plot and Visualization (Chap 9) 第 7 週 Functions and Loops 第 8 週 Data Loading (Chap 6) 第 9 週 Handling of Missing Data in Pandas (Chap 7) 第 10 週 Data Wrangling: Sort, Merge, Concatenate (Chap 8) 第 11 週 [Part 2: AI Programming] Simple Machine Learning and Deep Learning 第 12 週 Deep Learning, CNN 第 13 週 Final Project Presentation I 第 14 週 Final Project Presentation II 第 15 週 Real Case Discussion 第 16 週 Other Issues for the Python Programming