NTU Course
Overview

Artificial Intelligence Programming with Python - For Beginners

Offered in 115-1

  • Serial Number

    28035

  • Course Number

    IMPS1004

  • Course Identifier

    H41 10040

  • Class 01
  • 3 Credits

  • A6

    No Target Students

      A6

    • No Target Students

  • CHEN, YAN-BIN
  • Wed 7, 8, 9

  • 綜302

  • Type 2

  • 60 Student Quota

    NTU 56 + non-NTU 4

  • No Specialization Program

  • English
  • NTU COOL
  • Notes

    The course is conducted in English。。A6:Mathematics and Computer Science

  • NTU Enrollment Status

    Enrolled
    0/56
    Other Depts
    0/0
    Remaining
    0
    Registered
    0
  • Course Description
    *** Notice *** Kindly note that there is no need to send me an email for course enrollment. If you would like to take the course but are unable to enroll successfully, please come to class in the first week or second week to receive an authorization code. If you are an Electrical Engineering or Computer Science (EECS) student or already have experience with Python programming, you are likely to get bored in this course, as the content is specifically designed for non-EECS beginners. == Fall 2026 == This course is a practical programming class focused on artificial intelligence (AI) applications. Students are taught introductory Python at the beginning, engage in hands-on programming in class, and implement AI examples in the final month. This course is specifically designed for beginners and covers basic concepts of the Python programming language. We also cover a few principles behind why computers run programs this way. The examples and exercises provided in the course primarily emphasize AI applications. Finally, students will use Python to implement a final project, which includes programming tasks (with hints, if necessary), and present their work. Teaching methods in each week: 80 mins: Lecture. 70 mins: Students engage in hands-on exercises and teamwork. You may use AI tools to assist you with the exercises.
  • Course Objective
    (1)Students are expected to have hands-on programming experience in the Python language. (2)Students will be able to showcase their artificial intelligence programs or data analysis developed in Python through their final projects.
  • Course Requirement
    The students should take along with their laptops in the class session.
  • Expected weekly study hours before and/or after class
    0.5 hours
  • Office Hour
    *This office hour requires an appointment
  • Designated Reading
    As shown in References.
  • References
    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/)
  • Grading
    10%

    Interaction

    Q and A in class session

    40%

    Exercise

    Exercises in class

    50%

    Final

    Final project

    1. NTU recommends an upper limit of 20% for A+ grades. This is not a mandatory requirement. Instructors may adjust the percentage based on course requirements. Instructors teaching required courses are particularly encouraged to follow this guideline.
    2. NTU uses a letter grade system for assessment. The grade percentage ranges and the single-subject grade conversion table in the NATIONAL TAIWAN UNIVERSITY Regulations Governing Academic Grading are for reference only. Instructors may adjust the percentage ranges according to the grade definitions. For more information, see the Assessment for Learning Section。
  • Adjustment methods for students
    Adjustment MethodDescription
    A3

    提供學生彈性出席課程方式

    Provide students with flexible ways of attending courses

    B1

    延長作業繳交期限

    Extension of the deadline for submitting assignments

    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

    D1

    由師生雙方議定

    Negotiated by both teachers and students

  • Make-up Class Information
  • Course Schedule
    9/09Week 1Introduction
    9/16Week 2[Phase 1: Basic Python for Beginners] Introduction to Python and Environment Setup
    9/23Week 3Python Syntax
    9/30Week 4Data Types
    10/07Week 5If-else, Loops, and File Read/Write
    10/14Week 6Pandas
    10/21Week 7Pandas
    10/28Week 8Plot and Visualization
    11/04Week 9Data Wrangling in Pandas: Sort, Merge, and Concatenate
    11/11Week 10[Phase 2: AI Programming] Artificial Intelligence: Machine Learning
    11/18Week 11Artificial Intelligence: Supervised Learning (ex:CNN)
    11/25Week 12Artificial Intelligence: Unsupervised Learning (ex: K-Means)
    12/02Week 13Vibe Coding
    12/09Week 14Final Project Presentation I
    12/16Week 15Final Project Presentation II
    12/23Week 16Drop-In Discussion Session: Special Issues
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