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- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
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School of Environment and Society
- Department of Architecture and Building Engineering
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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 Industrial Engineering and Economics
- Instructor(s)
- Ichise Ryutaro
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- Day/Period(Room No.)
- Tue3-4(W9-324(W933)) Fri3-4(W9-324(W933))
- Group
- -
- Course number
- IEE.A204
- Credits
- 2
- Academic year
- 2024
- Offered quarter
- 1Q
- Syllabus updated
- 2024/3/14
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
This lecture will introduce probability models and analysis and reasoning methods to handle phenomena involving uncertainty appropriately. First, mathematical formulations of probability distributions will be presented based on the calculation methods of probability learned in high school. Next, we will discuss what kind of probability models can be used to describe uncertain phenomena found in nature and society. Furthermore, probabilistic reasoning, which uses probability to make inferences from occurring phenomena, will also be explained.
In problems such as business analysis and decision-making, it is necessary to handle uncertain phenomena appropriately. This lecture aims to acquire the basic knowledge to analyze and make inferences using probability theory for such problems.
Student learning outcomes
By taking this course, students will be able to acquire the following skills.
(1) Basic knowledge of probability, probability distributions, and probabilistic reasoning.
(2) To be able to utilize probabilistic analysis and reasoning to solve engineering problems.
(3) To be able to apply probabilistic views and ideas to real-world problems.
Keywords
random variables, probability distribution, conditional probability, binomial distribution, stochastic process, probabilistic reasoning, Naïve Bayes, Bayesian networks
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Give a lecture and give some exercise problems. Solutions for the exercise problems are also reviewed
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Probability (1) | Understand sets and numbers, permutations, and combinations |
| Class 2 | Probability (2) | Understand axiomatic probabilities |
| Class 3 | Probability (3) | Understand Conditional Probability |
| Class 4 | Probability density function and moments | Understand probability density function and moments |
| Class 5 | Probability distributions (1) | Understand basic probability distributions |
| Class 6 | Probability distributions (2) | Understand various probability distributions |
| Class 7 | Joint probability distribution | Understand Joint probability distribution |
| Class 8 | Representation of events and probability | Understand representation method of events and probability in AI |
| Class 9 | Probabilistic Reasoning (1) | Understand basic idea of probabilistic reasoning |
| Class 10 | Probabilistic Reasoning (2) | Understand Naïve Bays |
| Class 11 | Probabilistic Reasoning (3) | Understand Bayesian Networks |
| Class 12 | Stochastic process (1) | Understand basic idea of stochastic process |
| Class 13 | Stochastic process (2) | Understand advanced idea of stochastic process |
| Class 14 | Conclusion | Understand how to apply probabilistic models to engineering problems |
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.
Textbook(s)
Nobuaki Obata: Probability and Statistics for Data Science, Kyoritsu Shuppan (in Japanese)
Reference books, course materials, etc.
Stuart Russell, Peter Norvig: Artificial Intelligence: A Modern Approach, Pearson
Kazunori Matsumoto, Tetsuhiro Miyahara, Yasuo Nagai, Ryutaro Ichise: Artificial Intelligence, Ohm Sha (in Japanese)
Provide handouts when needed.
Assessment criteria and methods
Exercise problems and Final exam.
Related courses
- IEE.A205 ： Statistics for Industrial Engineering and Economics
- IEE.A331 ： OR and Modeling
- IEE.C302 ： Quality Management
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Nothing in particular.
