Course title
L00355003
AI Programming

 WATABE Shohei
Course description
Students will learn programming techniques related to artificial intelligence. For this purpose, students will first learn the Python language, which is commonly used in artificial intelligence programming, from the basics of syntax to object-oriented programming and the use of major libraries. Then, students will learn how to express concepts and algorithms such as serach, deep learning, and reinforcement learning in Python programs.
Purpose of class
Learn the programing method of artificial intelligence with the use of Python.
Goals and objectives
  1. To develop the efficient program by using Python.
  2. To write codes of search algorithm as an artificial intellignece programming.
  3. To write codes of deep learning as an artificial intellignece programming.
  4. To write codes of reinforcement learning as an artificial intellignece programming.
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 Reports Total.
1. 25% 25%
2. 25% 25%
3. 25% 25%
4. 25% 25%
Total. 100% -
Evaluation method and criteria
Grading: Reports(100%). Over 60% in total is acceptable.
If you can understand and explain the fundamentals of how to program in Python, and you write executable programs for search tasks, deep learning, and reinforcement learning, you will be able to achieve a total score of 60% or higher.
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Introduction
・Building Python development environment
・Python syntax 		Preparation: Read the relevant sections of the reference book and lecture materials in advance. 95minutes
Review class assignments 95minutes
Make a report 190minutes
2. Search 1.
・Tit-tac-toe by minimax method
・Tit-tac-toe by alpha-beta method 		Preparation: Read the relevant sections of the reference book and lecture materials in advance. 95minutes
Review class assignments 95minutes
Make a report 190minutes
3. Search 2.
・Tit-tac-toe by Atomic Monte Carlo
・Tit-tac-toe by Monte Carlo Tree Search 		Preparation: Read the relevant sections of the reference book and lecture materials in advance. 95minutes
Review class assignments 95minutes
Make a report 190minutes
4. Reinforcement Learning 1.
・multi-armed bandit problem
・maze game with policy gradient method 		Preparation: Read the relevant sections of the reference book and lecture materials in advance. 95minutes
Review class assignments 95minutes
Make a report 190minutes
5. Deep learning 1.
・Classification with neural networks
・Regression of neural networks 		Preparation: Read the relevant sections of the reference book and lecture materials in advance. 100minutes
Review class assignments 90minutes
Make a report 190minutes
6. Deep learning 2.
・Convolutional neural network image classification
・ResNet (Residual Network) image classification 		Preparation: Read the relevant sections of the reference book and lecture materials in advance. 95minutes
Review class assignments 95minutes
Make a report 190minutes
7. Reinforcement learning 2.
・maze game with Sarsa and Q-learning
・CartPole with DQN (deep Q-network) 		Preparation: Read the relevant sections of the reference book and lecture materials in advance. 95minutes
Review class assignments 95minutes
Make a report 190minutes
Total. - - 2660minutes
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback in the class
Textbooks and reference materials
Textbook: "AlphaZero Deep Learning, Reinforcement Learning, and Search Artificial Intelligence Programming" Eiichi Furukawa Bourne Digital
Prerequisites
Students should take a course on artificial intelligence.
Office hours and How to contact professors for questions
  • During the lunch break on lecture days, or via email.
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
  • Course that cultivates a basic problem-solving skills
Active-learning course
Most classes are interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A N/A
Education related SDGs:the Sustainable Development Goals
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
  • 12.RESPONSIBLE CONSUMPTION & PRODUCTION
Last modified : Thu Mar 06 10:05:35 JST 2025