2020 Probability and Statistics (ICT)

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Academic unit or major
Undergraduate major in Information and Communications Engineering
Instructor(s)
Uyematsu Tomohiko  Nagai Takehiro 
Course component(s)
Lecture / Exercise
Mode of instruction
ZOOM
Day/Period(Room No.)
Mon3-6(S422)  Thr5-6(S422)  
Group
-
Course number
ICT.M202
Credits
3
Academic year
2020
Offered quarter
1Q
Syllabus updated
2020/9/18
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course focuses on the fundamentals of probability and statistics which are used in various research areas such as model for digital communication, analysis of genome, and statistical analysis of big data. This course provides not only mathematical foundation of probability and statistics, but also practical methods to apply these mathematical knowledge.

Student learning outcomes

At the end of this course, students will be able to understand the following concepts:
1) The probability theory (probability axioms, expected value, variance, and moment generating function)
2) Multidimensional probability distribution, statistical independence, and correlation
3) Normal distribution and binomial distribution
4) Law of large numbers and central limit theorem
5) Hypothesis testing, point estimation, interval estimation
6) Bayesian statistics

Keywords

probability axioms, expected value, variance, moment generating function, multidimensional probability distribution, statistical independence, correlation, normal distribution, binomial distribution, law of large numbers, central limit theorem, hypothesis testing, point estimation, interval estimation, Bayesian statistics

Competencies that will be developed

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

Class flow

Towards the end of class, students are given exercise problems related to what is taught on that day to solve. In the exercise class, students are given advanced or practical problems related to the previous lecture classes.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Lecture 1: Definition of probability and conditional probability Peruse chapters 1 and 2 of the textbook.
Class 2 Exercise 1 Review Lectures 1.
Class 3 Lecture 2:Bayes' theorem and random variables Peruse the latter half of chapter 2 and the first half of chapter 3 of the textbook.
Class 4 Lecture 3: Random variables 2 Peruse the last half of chapter 3 of the textbook.
Class 5 Exercise 2 Review Lectures 2 and 3.
Class 6 Lecture 4: Multi-dimensional random variable Peruse the last half of chapter 3 of the textbook.
Class 7 Lecture 5: Binomial distribution and Poisson distribution Peruse the first half of chapter 4 of the textbook.
Class 8 Exercise 3 Review Lectures 4 and 5
Class 9 Lecture 6: Normal distribution and central limit theorem Peruse the last half of chapter 4 of the textbook.
Class 10 Lecture 7: Distribution of samples and statistics Peruse the first half of chapter 5 of the textbook.
Class 11 Exercise 4 Review Lectures 6 and 7 .
Class 12 Mid-term examination Review Lectures 1-6.
Class 13 Lecture 8: Normal population Peruse the last half of chapter 5 of the textbook.
Class 14 Exercise 5 Review Lectures 8.
Class 15 Lecture 9: Statistical estimation Peruse Section 6.1 of the textbook.
Class 16 Lecture 10: Interval estimation and confidence level Peruse Section 6.2 of the textbook.
Class 17 Exercise 6 Review Lectures 9 and 10.
Class 18 Lecture 11: Interval estimation 2 Peruse Section 6.2 of the textbook.
Class 19 Lecture 12: Hypothesis testing Peruse Sections 6.3, 6.4 and 6.5 of the textbook.
Class 20 Exercise 7 Review Lectures 11 and 12.
Class 21 End-term examination Review Lectures 7-12.

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend a certain length of time outside of class on preparation and review (including for assignments), as specified by the Tokyo Institute of Technology Rules on Undergraduate Learning (東京工業大学学修規程) and the Tokyo Institute of Technology Rules on Graduate Learning (東京工業大学大学院学修規程), for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

Junkichi Satsuma, Probability and Statistics, Iwanami, 2019. (Japanese)

Reference books, course materials, etc.

Materials used in class can be found on OCW-i.

Assessment criteria and methods

Student learning outcomes are evaluated by the results of exercises (20%), small examinations (40%), and the final examination (40%).

Related courses

  • LAS.M101 : Calculus I / Recitation
  • LAS.M105 : Calculus II
  • LAS.M107 : Calculus Recitation II
  • LAS.M102 : Linear Algebra I / Recitation
  • LAS.M106 : Linear Algebra II
  • LAS.M108 : Linear Algebra Recitation II

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

No prerequisites.

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