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School of Environment and Society
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- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Undergraduate major in Computer Science
- Instructor(s)
- Kanezaki Asako
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- Day/Period(Room No.)
- Tue3-4(WL2-201(W621)) Fri3-4(WL2-201(W621))
- Group
- -
- Course number
- CSC.T272
- Credits
- 2
- Academic year
- 2024
- Offered quarter
- 2Q
- Syllabus updated
- 2024/3/26
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
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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, Dynamic programming, Probabilistic inference, Bayesian network, Decision tree, Markov decision processes, Reinforcement learning, Bayesian filters, Monte Carlo methods, Natural language processing, Symbolic logic
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
1) Elective assignments will be given during the lecture.
2) Attendance is taken in every class.
3) Students are recommended to learn the topics by themselves before coming to class.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Chapter 1: Let's Create Artificial Intelligence, Chapter 2: Search (1): State Space and Basic Search | Explain in the class. |
| Class 2 | Chapter 3: Search (2): Searching for the optimal route | Explain in the class. |
| Class 3 | Chapter 4: Search (3): Theory of Games | Explain in the class. |
| Class 4 | Chapter 5: Planning and Decision Making (1): Dynamic Programming | Explain in the class. |
| Class 5 | Chapter 6: Stochastic Models (1): Basics of Probability and Bayesian Theory | Explain in the class. |
| Class 6 | Chapter 7: Stochastic Models (2): Stochastic Generative Models and Naive Bayes | Explain in the class. |
| Class 7 | Chapter 8: Planning and Decision Making (2): Reinforcement Learning | Explain in the class. |
| Class 8 | Chapter 9: State Estimation (1): Bayesian Filters | Explain in the class. |
| Class 9 | Chapter 10 State Estimation (2): Particle Filters | Explain in the class. |
| Class 10 | Chapter 14 Language and Logic (1): Natural Language Processing | Explain in the class. |
| Class 11 | Chapter 15 Language and Logic (2): Symbolic Logic | Explain in the class. |
| Class 12 | Chapter 16 Language and Logic (3): Proof and Question Answering, Chapter 17 Summary: "Creating" Intelligence | Explain in the class. |
| Class 13 | Presentation of Project Assignments (1) | Explain in the class. |
| Class 14 | Presentation of Project Assignments (2) | Explain in the class. |
Out-of-Class Study Time (Preparation and Review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterward (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
See reference book
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 assignments 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.T242 ： Probability Theory and Statistics".
Other
None.
