### 2016　Probability Theory and Statistics

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Computer Science
Instructor(s)
Matsumoto Ryutaroh
Class Format
Lecture
Media-enhanced courses
Day/Period(Room No.)
Mon7-8(W933)
Group
O
Course number
ZUS.M201
Credits
2
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

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