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Python人工智慧程式設計入門

113-2 開課異動

  • 流水號

    35842

  • 課號

    IMPS1004

  • 課程識別碼

    H41 10040

  • 班次 02
  • 3 學分

  • A6

    無授課對象

      A6

    • 無授課對象

  • 陳彥賓
  • 三 A, B, C

  • 普103

  • 2 類加選

  • 修課總人數 50 人

    本校 46 人 + 外校 4 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

    載入中
  • 課程概述
    *** Notice *** Kindly notice that there is no need to send me an email for course enrollment. If you would like to take the course but were unable to successfully enroll, please come to class in the first week. We may deliver the authorization codes. The unsuccessful enrollment status will be announced after the preliminary course selection on January 17th. == Spring 2025 == The course is a practical programming class focused on artificial intelligence (AI) examples. Students are taught introductory Python language at the beginning, engage in hands-on programming in class, and implement AI examples in the final month of the course. If you are an Electrical Engineering or Computer Science (EECS) student or already have experience with Python programming, you’re most likely to be bored in this course, as it is specifically designed for non-EECS beginners. The course covers basic to advanced concepts of the Python programming language. The examples and exercises provided in the course primarily emphasize AI applications. Finally, students will use Python to implement the final project, which contains programming tasks (with hints, if necessary), and present their work. The course is taught in English, but bilingual Q&A sessions are acceptable. Teaching methods in each week: 70 mins: Lecture. 70 mins: Students engage in hands-on exercises and teamwork. You may use AI tools to assist you with the exercises. 10 mins: Conclusion of hands-on exercises and fundamental knowledge.
  • 課程目標
    (1)Students are expected to have hands-on programming experience in the Python language. (2)By the end of the curriculum, students will be able to showcase their artificial intelligence programs or data analysis developed in Python through their final projects.
  • 課程要求
    The students should take along with their laptops in the class session.
  • 預期每週課後學習時數
    2 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 and presentation

  • 針對學生困難提供學生調整方式
    調整方式說明
    上課形式

    提供學生彈性出席課程方式

    作業繳交方式

    學生與授課老師協議改以其他形式呈現

    考試形式

    書面(口頭)報告取代考試

    其他

    由師生雙方議定

  • 課程進度
    2/19第 1 週Introduction
    2/26第 2 週[Part 1: Basic Python for Beginners] Introduction to Python and Environment Setup
    3/05第 3 週Python Syntax
    3/12第 4 週Data Types, and Functions
    3/19第 5 週If-else, Loops, and File Read/Write
    3/26第 6 週Pandas
    4/02第 7 週Plot and Visualization
    4/09第 8 週Handling of Missing Data
    4/16第 9 週Data Wrangling: Sort, Merge, and Concatenate
    4/23第 10 週[Part 2: AI Programming] Artificial Intelligence: Machine Learning
    4/30第 11 週Artificial Intelligence: Deep Learning (ex:CNN)
    5/07第 12 週Artificial Intelligence: Clustering (ex: K-Means)
    5/14第 13 週Artificial Intelligence: Recurrent Neural Network (ex: LSTM)
    5/21第 14 週Final Project Presentation I
    5/28第 15 週Final Project Presentation II
    6/04第 16 週Special Issues for the Python Programming