2021 Spring
[Announcement]
Please visit https://ncueeclass.ncu.edu.tw/
[Couse information]
In the past 20 years, the AI community has successfully applied machine learning technologies
to real world problems. The objective of this course is to teach students how to formulate a
real world problem and solve it using AI techniques. In particular, the course will focus on
heuristic search, perception, decision-making and machine learning (e.g., supervised learning, unsupervised learning and reinforcement learning). The students will have intensive hand-on training about how to solve AI problems.
Instructor: Prof. Kuo-Shih Tseng
Time & classroom: Wed 789, M-430
Office hour: Th M5 (12pm-2pm)
Office: Room 407, Hong-Jing building
[Requirements]
Familiarity of probability, linear algebra, and programming (e.g., C/C++, Python or MATLAB).
Due to the limited number of robots, only 20~30 students can take this course.
If you are interested in this course, please attend the first lecture.
If you would like to take this course, please send me (kuoshih@math.ncu.edu.tw) the following documents by 2/26(Fri.):
A. 1-page SOP to explain why you want to take this course
B. Your transcript to demonstrate your ability in program, probability and linear algebra
C. Other supporting documents (e.g., programming competition award)
If you are qualified, I will give you a card to enroll in this course.
If you cannot enroll in this course, you still can be an audience.
However, you cannot use the robots & I will not grade your HW.
[Schedule]
[Textbook]
Prof. Kuo-Shih Tseng’s slides.
Optional textbooks:
Stuart Russell and Peter Norvig,“Artificial intelligence: a modern approach,” Pearson Education, 3rd edition, 2010.
Sebastian Thrun, Wolfram Burgard and Dieter Fox, “Probabilistic Robotics”, MIT Press, 2006.
[Grade]
The grade for this course will consist of the following components:
Homework (X3) 30%
Midterm Exam 30%
Project Proposal (1-2 page) 10%
Project Presentation and Demonstration 10%
Project Report (4-8 pages) 20%
In the final project, the students need to propose an interesting application of AI, formulate the problem, implement algorithms on a real robot (e.g., Minibot or Bebop).
2020 Spring
[Announcement]
Please visit https://lms.ncu.edu.tw/ for more information.
[Couse information]
In the past 20 years, the AI community has successfully applied machine learning technologies
to real world problems. The objective of this course is to teach students how to formulate a
real world problem and solve it using AI techniques. In particular, the course will focus on
heuristic search, perception, decision-making and machine learning (e.g., supervised learning, unsupervised learning and reinforcement learning). The students will have intensive hand-on training about how to solve AI problems.
Instructor: Prof. Kuo-Shih Tseng
Time & classroom: Mon 789, M-219
Office hour: Mon 23 (9am-11am)
Office: Room 407, Hong-Jing building
[Requirements]
Familiarity of probability, linear algebra, and programming (e.g., C/C++, Python or MATLAB).
Due to the limited number of robots, only 15~20 students can take this course.
If you are interested in this course, please attend the first lecture.
If you would like to take this course, please send me (kuoshih@math.ncu.edu.tw) the following documents by 3/06(Fri.):
A. 1-page SOP to explain why you want to take this course
B. Your transcript to demonstrate your ability in program, probability and linear algebra
C. Other supporting documents (e.g., programming competition award)
If you are qualified, I will give you a card to enroll in this course.
If you cannot enroll in this course, you still can be an audience.
However, you cannot use the robots & I will not grade your HW.
[Schedule]
[Textbook]
Prof. Kuo-Shih Tseng’s slides.
Optional textbooks:
Stuart Russell and Peter Norvig,“Artificial intelligence: a modern approach,” Pearson Education, 3rd edition, 2010.
Sebastian Thrun, Wolfram Burgard and Dieter Fox, “Probabilistic Robotics”, MIT Press, 2006.
[Grade]
The grade for this course will consist of the following components:
Homework (X3) 30%
Midterm Exam 30%
Project Proposal (1-2 page) 10%
Project Presentation and Demonstration 10%
Project Report (4-8 pages) 20%
In the final project, the students need to propose an interesting application of AI, formulate the problem, implement algorithms on a real robot (e.g., Minibot or Bebop).
2019 Spring
[Announcement]
Please visit https://lms.ncu.edu.tw/ for more information.
[Couse information]
In the past 20 years, the AI community has successfully applied machine learning technologies
to real world problems. The objective of this course is to teach students how to formulate a
real world problem and solve it using AI techniques. In particular, the course will focus on
heuristic search, perception, decision-making and machine learning (e.g., supervised learning, unsupervised learning and reinforcement learning). The students will
have intensive hand-on training about how to solve AI problems.
Instructor: Prof. Kuo-Shih Tseng
Time & classroom: Mon 789, M-219
Office hour: Mon 23 (9am-11am)
Office: Room 407, Hong-Jing building
[Requirements]
Familiarity of probability, linear algebra, and programming (e.g., C/C++, Python or MATLAB).
Due to the limited number of robots, only 20 students can take this course.
If you are interested in this course, please attend the first lecture.
If you would like to take this course, please send me (kuoshih@math.ncu.edu.tw) the following documents by 2/22(Fri.):
A. 1-page SOP to explain why you want to take this course
B. Your transcript to demonstrate your ability in program, probability and linear algebra
C. Other supporting documents (e.g., programming competition award)
If you are qualified, I will give you a card to enroll in this course.
If you cannot enroll in this course, you still can be an audience.
However, you cannot use the robots & I will not grade your HW.
[Schedule]
[Textbook]
Prof. Kuo-Shih Tseng’s slides.
Optional textbooks:
Stuart Russell and Peter Norvig,“Artificial intelligence: a modern approach,” Pearson Education, 3rd edition, 2010.
Sebastian Thrun, Wolfram Burgard and Dieter Fox, “Probabilistic Robotics”, MIT Press, 2006.
[Grade]
The grade for this course will consist of the following components:
Homework (X3) 30%
Midterm Exam 30%
Project Proposal (1-2 page) 10%
Project Presentation and Demonstration 10%
Project Report (4-8 pages) 20%
In the final project, the students need to propose an interesting application of AI, formulate the problem, implement algorithms on a real robot (e.g., Minibot or Bebop).
[Final Project Demo]