Serial Number
55704
Course Number
IMPS1004
Course Identifier
H41 10040
- Class 02
- 3 Credits
A6
No Target Students
No Target Students
A6- CHEN, YAN-BIN
- View Courses Offered by Instructor
COMMON GENERAL EDUCATION CENTER Master Program in Statistics of National Taiwan University
yanbin@ntu.edu.tw
- Room 212, Chee-Chun Leung Cosmology Hall (次震宇宙館 212室)
02-33664688
Website
https://sites.google.com/view/yan-bin/home
- Tue A, B, C
Putong Lecture Building Rm.304 (普304)
Type 2
40 Student Quota
NTU 40
No Specialization Program
- English
- NTU COOL
- NotesThe course is conducted in English。For non-EECS college students.。A6:Mathematics, Digital Competence, and Quantitative Analysis
NTU Enrollment Status
Loading...- Course DescriptionThe 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.
- Course ObjectiveAt 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.
- Course RequirementThe students should take along with their laptops in the class session.
- Expected weekly study hours after class4 hours
- Office Hour
*This office hour requires an appointment - Designated ReadingMonth 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
- ReferencesBook 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 (peer evaluation 10%)
- Adjustment methods for students
Adjustment Method Description Teaching methods Provide students with flexible ways of attending courses
Assignment submission methods Group report replace Personal report
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
Week 1 Introduction Week 2 [Part 1: Basic Python for Beginners] Introduction to Python & Environment Setup (Chap 2) Week 3 Python Syntax and Data Structure (Chap 3) Week 4 Array and Vectorized Computations for Common Data Processing Tasks (Chap 4) Week 5 Pandas (Chap 5) Week 6 Plot and Visualization (Chap 9) Week 7 Functions and Loops Week 8 Data Loading (Chap 6) Week 9 Handling of Missing Data in Pandas (Chap 7) Week 10 Data Wrangling: Sort, Merge, Concatenate (Chap 8) Week 11 [Part 2: AI Programming] Simple Machine Learning and Deep Learning Week 12 Deep Learning, CNN Week 13 Final Project Presentation I Week 14 Final Project Presentation II Week 15 Real Case Discussion Week 16 Other Issues for the Python Programming