NTU Course
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Artificial Intelligence Programming with Python - For Beginners

Offered in 113-2Updated
  • Notes
    The course is conducted in English。For non-EECS college students.。A6:Mathematics, Digital Competence, and Quantitative Analysis

  • NTU Enrollment Status

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  • Course Description
    *** 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.
  • Course Objective
    (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.
  • Course Requirement
    The students should take along with their laptops in the class session.
  • Expected weekly study hours after class
    2 hours
  • Office Hour
    *This office hour requires an appointment
  • Designated Reading
    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
  • 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
    50%

    In class

    Exercise in class session

    50%

    Final

    Final project and presentation

  • Adjustment methods for students
    Adjustment MethodDescription
    Teaching methods

    Provide students with flexible ways of attending courses

    Assignment submission methods

    Mutual agreement to present in other ways between students and instructors

    Exam methods

    Written (oral) reports replace exams

    Others

    Negotiated by both teachers and students

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