2019 Artificial Intelligence

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Academic unit or major
Undergraduate major in Computer Science
Instructor(s)
Shinoda Koichi 
Course component(s)
Lecture
Day/Period(Room No.)
Mon7-8(W621)  Thr7-8(W621)  
Group
-
Course number
CSC.T272
Credits
2
Academic year
2019
Offered quarter
4Q
Syllabus updated
2019/3/18
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course gives the fundamentals for understanding artificial intelligence systems and their components. First, the students learn how to formulate problems and how to search for their solution. Then they learn how to explicitly represent knowledge and how to do inference based on it. Further, they learn planning for efficient inference. Finally, they learn machine learning in which machine automatically acquire knowledge.

Student learning outcomes

By the end of this course, students will be able to:
1) Understand the necessity of artificial intelligence systems which support human intellectual activities in the information society.
2) Aquire elemental techniques used for building artificial intelligence systems.
3) Represent the process of human's intellectual production.
4) Do inferences based on the representation.

Keywords

State space representation, Graph search, Heuristic search, A* search, Game, Minimax method, α-β pruning, Semantic network, Frame, Production system, Resolution principle, Forward inference, Backward inference, Default logic, Probabilistic inference, Bayesian network, GPS, Hierarchical planning, Partial order planning, Reactive planning, Linear classifier, Neural network, Decision tree

Competencies that will be developed

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills

Class flow

1) At the beginning of each class, the contents of the previous class are reviewed.
2) At the end of each class, an assignment is given, which should be submitted in the next class.
3) Attendance is taken in every class.
4) Students are recommended to learn the topics by themselves before coming to class.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction Explain in the class.
Class 2 Search 1: State space representation, Graph search Explain in the class.
Class 3 Search 2: Heuristic search, A* search Explain in the class.
Class 4 Search 3: Game (Minimax method, α-β pruning) Explain in the class.
Class 5 Knowledge representation 1: Semantic network, Frame Explain in the class.
Class 6 Knowledge representation 2: Production system Explain in the class.
Class 7 Inference 1: Resolution principle Explain in the class.
Class 8 Inference 2: Forward and backward inference, Default logic Explain in the class.
Class 9 Inference 3: Probabilistic inference (Bayesian network) Explain in the class.
Class 10 Planning 1: GPS, Hierarchical planning Explain in the class.
Class 11 Planning 2: Partial order planning, Reactive planning Explain in the class.
Class 12 Machine learning 1: Linear classifier Explain in the class.
Class 13 Machine learning 2: Neural network Explain in the class.
Class 14 Machine learning 3: Decision tree, misc Explain in the class.
Class 15 Future prospects Explain in the class.

Textbook(s)

None.

Reference books, course materials, etc.

Russel and Norvig, "Artificial Intelligence: A Modern Approach (3rd Edition)", Pearson

Assessment criteria and methods

Students course scores are based on an assignment in every class (20% in total) and final exam (80%).

Related courses

  • CSC.T352 : Pattern Recognition
  • CSC.T261 : Logic in Computer Science
  • CSC.T242 : Probability Theory and Statistics

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Students are expected to have taken "CSC.T261 : Logic in Computer Science" and "CSC.T242 : Probability Theory and Statistics".

Other

None.

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