| Course title | |||||
| 人工知能特論 [Selected Topics in Artificial Intelligence] | |||||
| Course category | courses for master's programs | Requirement | Credit | 2 | |
| Department | Year | ～ | Semester | 1st | |
| Course type | 1st | Course code | 1060613 | ||
| Instructor(s) | |||||
| 藤田 桂英 [FUJITA Katsuhide] | |||||
| Facility affiliation | Faculty of Engineering | Office | Email address | ||
| Course description |
|
This lecture demonstrates some intelligence algorithms and AI systems. These topics range from Base technology to state-of-art one. In the first half of the class, the area of AI which was studied actively in 1980s’ is shown. For example, search algorithms are the one of an important problem-solving technique, and can solve the particular problems based on the heuristics. Also, the knowledge representations and Logic can be learned from leading to Prolog. In the second half of the class, the topics related to Natural Language Processing, Web Intelligence, and Software Agents are learned as explaining the algorithms and idea related to them. Google Classroom Code: sa7xiub |
| Expected Learning |
|
- Learning the whole knowledge related to AI - Learning the ability to generate the AI system See the Curriculum maps |
| Course schedule |
|
(1st half of class: 1-7) - What’s AI?: History of AI, Turing Test, Learning Machine, AI and related areas - Search and Problem Solving: State Space, Depth-first Search, Width-first Search, Recursion Algorithm, Best-first Search - AI with Game: Variety of Games, Game Tree, AND/OR Tree, min-max strategy, alpha-beta cutoff - Bio-Inspired Algorithm: Genetic Algorithm, Hill-Climbing, Simulated Annealing - Knowledge Representation: Semantic Network, Frame, Hierarchy, Inheritance, Relationship between Knowledge Representation and NLP - Predicate Logic: Propositional Logic, Predicate Logic, Resolution, Logical Reasoning （2nd half of class: 8-14） - Natural Language Processing: Corpus, thesaurus, Morphological Analysis, Statistical natural language processing, Document classification, Information Retrieval, Text Mining - Web Intelligence: Search Engine (Indexing, Ranking, Crawling), Community Formation (Complex Network), Recommendation System and Collective Intelligence, Web programming - Software Agent: What’s Agent? Cooperation (Problem Solving, Search, Action Selection), Negotiation, Market-based Mechanism, Auction, Learning, Complex System |
| Prerequisites |
| In addition to 30 hours that students spend in the class, students are recommended to prepare for and revise the lectures, spending the standard amount of time as specified by the University and using the lecture handouts as well as the references specified below. |
| Required Text(s) and Materials |
| None |
| References |
| - S.J.Russell, P.Norvig, “Artificial Intelligence: A Modern Approach” |
| Assessment/Grading |
|
The grade evaluation in this online class is premised on all attendances, and comprehensively evaluates the attitude to learn, quizzes, report, and online tests. The standard study time set by the our university is required to get the grade. The rate of evaluation is as follows: Small Quiz, reports, and Examination (100%) Grade will be given according to the following criteria by comprehensive evaluation: S: 90 points or more, A: 80 or more and less than 90 points, B: 70 or more and less than 80 points, C: 60 or more and less than 70 points. |
| Message from instructor(s) |
| Through the AI class, you can learn the way that computers solve the complex problems with the intelligence. In addition, you can learn some important AI algorithms which work in the society. |
| Course keywords |
| Problem Solving, Game-Tree Search, Knowledge Representation, Natural Language Processing, Web Intelligence, Software Agents |
| Office hours |
| Please e-mail me when you have questions. |
| Remarks 1 |
| Remarks 2 |
| Related URL |
| Lecture Language |
| Japanese |
| Language Subject |
| Last update |
| 3/11/2022 3:34:54 PM |