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- Academic unit or major
- Computer Science
- Instructor(s)
- Ishida Takashi
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Thr7-8(S622) Thr7-8(S621)
- Group
- E
- Course number
- ZUS.M201
- Credits
- 2
- Academic year
- 2016
- Offered quarter
- 1-2Q
- Syllabus updated
- 2017/1/11
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
The instructor will lecture on the basics of probability and statistics. The aim of this course is for students to understand the principles of techniques based on probability theory and statistics which are frequently applied in information engineering, and learn various calculation methods.
Student learning outcomes
The instructor will lecture on the basics of probability and statistics. The aim of this course is for students to understand the principles of techniques based on probability theory and statistics which are frequently applied in information engineering, and learn various calculation methods.
Keywords
Concepts of probability theory (conditional probability, expected value, variance), probability distribution (binomial distribution, normal distribution), probability laws (Chebyshev's inequality, convergence theorem), statistical inference, hypothesis testing
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
This course mainly consists of lectures. The midterm and final exams will be set. A problem set may be given in class.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | The basics of probability theory and statistics | None |
| Class 2 | Random variables | None |
| Class 3 | Probability distribution | None |
| Class 4 | Moments | None |
| Class 5 | Moment-generating functions | None |
| Class 6 | Examples of discrete probability distributions | None |
| Class 7 | Examples of continuous probability distributions | None |
| Class 8 | Probability inequalities | None |
| Class 9 | Pseudorandom numbers | None |
| Class 10 | Stochastic process | None |
| Class 11 | Multidimensional probability distribution | None |
| Class 12 | Law of large numbers | None |
| Class 13 | Central limit theorem | None |
| Class 14 | Statistical inference | None |
| Class 15 | Statistical hypothesis testing | None |
Textbook(s)
Please refer to the corresponding Japanese description.
Reference books, course materials, etc.
Please refer to the corresponding Japanese description.
Assessment criteria and methods
Students will be assessed on their reports, the final exam etc.
Related courses
- ICT.C205 ： Communication Theory (ICT)
- ZUS.M303 ： Digital Communications
- ICT.H313 ： Sensation and Perception Systems
- CSC.T352 ： Pattern Recognition
- CSC.T353 ： Biological Data Analysis
Prerequisites (i.e., required knowledge, skills, courses, etc.)
None
