Artificial Intelligence

Serial Number
14733
Course Number
CSIE5400
Course Identifier
922 U3020
No Class
3 Credits
Elective
GRADUATE INSTITUTE OF COMPUTER SCIENCE & INFORMATION ENGINEERING / GRADUATE INSTITUTE OF NETWORKING AND MULTIMEDIA / Intelligent Medicine Program
- GRADUATE INSTITUTE OF COMPUTER SCIENCE & INFORMATION ENGINEERING
- GRADUATE INSTITUTE OF NETWORKING AND MULTIMEDIA
- Intelligent Medicine Program
WEN-HUANG CHENG
- View Courses Offered by Instructor
- COLLEGE OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE DEPARTMENT OF COMPUTER SCIENCE & INFOR
Wed 7, 8, 9
資103
Type 2
Add course with authorization code from the instructor
120 Student Quota
NTU 120
No Specialization Program
Chinese
NTU COOL
Core Capabilities and Curriculum Planning

Notes
Not open in course pre-registration period。
NTU Enrollment StatusNTU students only. Updated every five minutes during the course selection period.
Enrolled
0/120
Other Depts
0/0
Remaining
0
Registered
0
Course Description
Artificial Intelligence (AI) refers to the sophisticated capabilities of machines that mimic human cognitive functions, including reasoning, learning, planning, and creativity. This course is meticulously designed to offer a hands-on introduction to a broad spectrum of AI methodologies applicable to an array of problem-solving scenarios. It aims to equip participants with the insight to identify opportune moments and effective strategies for employing established AI techniques to address and resolve novel challenges.
Course Objective
Upon completing this course, students will have acquired the expertise to:  Construct and comprehend the mathematical underpinnings of rational, learning agents  Identify and implement appropriate AI methodologies across a diverse array of problem domains  Understand the application of these methodologies within contemporary AI systems  Develop the readiness to make informed decisions regarding the utilization of AI in societal contexts
Course Requirement
Expected weekly study hours after class
Office Hour
Designated Reading
References
Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach,” 4th edition, Pearson Education, 2021.
Grading
90%
Assignments
6 times, announced in class or from the course website: Tentatively, all Python programming assignments
10%
Class Participation
Only applicable for “Invited Talks”
Adjustment methods for students

Course Schedule

02/21Week 1	02/21	Course Overview
02/28Week 2	02/28	和平紀念日(放假日)
03/06Week 3	03/06	Modern AI: ◾Search ◾Knowledge ◾Uncertainty ◾Optimization ◾Learning
03/13Week 4	03/13	Modern AI (cont.)
03/20Week 5	03/20	Modern AI (cont.)
03/27Week 6	03/27	Modern AI (cont.)
04/03Week 7	04/03	Modern AI (cont.)
04/10Week 8	04/10	Modern AI (cont.)
04/17Week 9	04/17	Modern AI (cont.)
04/24Week 10	04/24	Modern AI (cont.)
05/01Week 11	05/01	Modern AI (cont.)
05/08Week 12	05/08	Modern AI (cont.)
05/15Week 13	05/15	New Wave of AI: ◾Generalist Agents ◾Multimodal Systems
05/22Week 14	05/22	New Wave of AI (cont.)
05/29Week 15	05/29	New Wave of AI (cont.)
06/05Week 16	06/05	Invited Talks

90% Assignments	6 times, announced in class or from the course website: Tentatively, all Python programming assignments
10% Class Participation	Only applicable for “Invited Talks”