I448 Distance Learning System (2009-)
Aims
This lecture deals with fundamentals of distance learning theory and technology based on findings of human intelligent processing and design/operation/evaluation of distance learning system.
Contents
Distance Learning, Learning Science, Human-Computer Interaction, Instructional Design, Synchronous Distance Learning System, Asynchronous Distance Learning System, Distance Learning Support Technology, Evaluation Methodology
Schedule
1. Introduction
2. Fundamentals of Distance Learning System 1
3. Fundamentals of Distance Learning System 2
4. Fundamentals of Distance Learning System 3
5. Practical Case Examples of Distance Learning System
6. Synchronous Distance Learning System 1
7. Synchronous Distance Learning System 2
8. Asynchronous Distance Learning System 1
9. Asynchronous Distance Learning System 2
10. Distance Learning Support Technology 1
11. Distance Learning Support Technology 2
12. Evaluation of Distance Learning System 1
13. Evaluation of Distance Learning System 2
14. Current Topics of Distance Learning System
15. Examination
2. Fundamentals of Distance Learning System 1
3. Fundamentals of Distance Learning System 2
4. Fundamentals of Distance Learning System 3
5. Practical Case Examples of Distance Learning System
6. Synchronous Distance Learning System 1
7. Synchronous Distance Learning System 2
8. Asynchronous Distance Learning System 1
9. Asynchronous Distance Learning System 2
10. Distance Learning Support Technology 1
11. Distance Learning Support Technology 2
12. Evaluation of Distance Learning System 1
13. Evaluation of Distance Learning System 2
14. Current Topics of Distance Learning System
15. Examination
I215 Artificial Intelligence(2009)
Aims
Artificial Intelligence (AI) is a discipline aiming at realizing intelligent behavior on computers. This lecture deals with formal treatments of human knowledge, automatic learning mechanisms for acquiring novel knowledge from various types of data and environments, and various search algorithms designed for solving various problems.
Contents
logic programming, non-monotonic reasoning, machine learning, search algorithm
Schedule
1. Logic Programming (1)
2. Logic Programming (2)
3. Non-monotonic Reasoning
4. Belief Revision
5. Logic of Rational Agents
6. Machine Learning, Version Space Method
7. Decision Trees
8. Supervised and Unsupervised Learning
9. Neural Network
10. Perceptron
11. Depth-first/Breadth-first Search
12. Heuristics Search (1)
13. Heuristics Search (2)
14. Minmax Search
15. Monte-Carlo Search
2. Logic Programming (2)
3. Non-monotonic Reasoning
4. Belief Revision
5. Logic of Rational Agents
6. Machine Learning, Version Space Method
7. Decision Trees
8. Supervised and Unsupervised Learning
9. Neural Network
10. Perceptron
11. Depth-first/Breadth-first Search
12. Heuristics Search (1)
13. Heuristics Search (2)
14. Minmax Search
15. Monte-Carlo Search
About Hasegawa
The main goal of our research is to facilitate "Human Learning and Computer-mediated Interaction" in distributed environment.