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Course title |
Artificial Intelligence Programming with Python - For Beginners |
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Semester |
112-2 |
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Designated for |
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Instructor |
CHEN, YAN-BIN |
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Curriculum Number |
IMPS1004 |
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Curriculum Identity Number |
H41E10040 |
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Class |
01 |
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Credits |
3.0 |
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Full/Half
Yr. |
Half |
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Required/
Elective |
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Time |
Tuesday 6,7,8(13:20~16:20) |
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Remarks |
The upper limit of the number of students: 40. |
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Course introduction video |
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Table of Core Capabilities and Curriculum Planning |
Association has not been established |
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Course Syllabus
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Please respect the intellectual property rights of others and do not copy any of the course information without permission
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Course Description |
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. |
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Course Objective |
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. |
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Course Requirement |
The students should take along with their laptops in the class session. |
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Student Workload (expected study time outside of class per week) |
4 hours |
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Office Hours |
Appointment required. |
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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 |
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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/) |
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Grading |
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No. |
Item |
% |
Explanations for the conditions |
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1. |
In class: exercise in class session |
50% |
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2. |
Final: final project (peer evaluation 10%) |
50% |
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Adjustment methods for students |
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Teaching methods |
Provide students with flexible ways of attending courses |
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Assignment submission methods |
Group report replace Personal report, Mutual agreement to present in other ways between students and instructors |
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Exam methods |
Written (oral) reports replace exams |
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Others |
Negotiated by both teachers and students |
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Week |
Date |
Topic |
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Week 1 |
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Introduction |
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Week 2 |
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[Part 1: Basic Python for Beginners]
Introduction to Python & Environment Setup (Chap 2) |
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Week 3 |
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Python Syntax and Data Structure (Chap 3) |
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Week 4 |
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Array and Vectorized Computations for Common Data Processing Tasks (Chap 4) |
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Week 5 |
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Pandas (Chap 5) |
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Week 6 |
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Plot and Visualization (Chap 9) |
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Week 7 |
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Functions and Loops |
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Week 8 |
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Data Loading (Chap 6) |
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Week 9 |
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Handling of Missing Data in Pandas (Chap 7) |
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Week 10 |
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Data Wrangling: Sort, Merge, Concatenate (Chap 8) |
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Week 11 |
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[Part 2: AI Programming]
Simple Machine Learning and Deep Learning |
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Week 12 |
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Deep Learning, CNN |
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Week 13 |
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Final Project Presentation I |
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Week 14 |
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Final Project Presentation II |
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Week 15 |
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Real Case Discussion |
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Week 16 |
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Other Issues for the Python Programming |