| Academic Year |
2025Year |
School/Graduate School |
Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program |
| Lecture Code |
WSN30601 |
Subject Classification |
Specialized Education |
| Subject Name |
情報科学特別講義F |
Subject Name
（Katakana） |
ジョウホウカガクトクベツコウギエフ |
Subject Name in
English |
Special Lecture on Informatics and Data Science F |
| Instructor |
See the class timetable. |
Instructor
(Katakana) |
ジュギョウジカンワリヲサンショウ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, First Semester, Intensive |
| Days, Periods, and Classrooms |
(Int) Inte |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Face-to-face |
| |
| Credits |
1.0 |
Class Hours/Week |
|
Language of Instruction |
E
:
English |
| Course Level |
6
:
Graduate Advanced
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
01
:
Mathematics/Statistics |
| Eligible Students |
|
| Keywords |
|
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
This lecture aims at providing a brief introduction to Bayesian inference in a more computational and applied manner. The lecture will be held in a workshop style in three days orientated by three main topics. The first day’s lecture starts with basics of Bayesian inference including inference for linear/ generalized linear regression problems and commonly used algorithms such as Markov chain Monte Carlo and Gibbs samplers. More advanced topics will be discussed in the following including classification and variable selection, Bayes factors and Bayesian model averaging. Main types of the priors for high-dimensional data will be discussed in detail, along with their properties and implementations to real data cases. At last, advanced Bayesian inference in nonparametric settings will be discussed in classification, density and regression problems. The lessons will be given based on lecture slides with hands-on coding examples in R for demonstration. |
| Class Schedule |
Lesson 1: Introduction to Bayesian inference in parametric settings
Lesson 2: Bayesian model selection, Bayes factors and Bayesian model averaging
Lesson 3: High-dimensional data, discrete sparsity induced priors
Lesson 4: Continuous shrinkage priors
Lesson 5: Dirichlet process mixture model and its application in clustering and estimation
Lesson 6: Gaussian processes and its recent developments in machine learning
lesson7
lesson8
lesson9
lesson10
lesson11
lesson12
lesson13
lesson14
lesson15 |
Text/Reference
Books,etc. |
“Bayesian Data Analyses”,
"Handbook of Bayesian Variable Selection" and reference papers within.
|
PC or AV used in
Class,etc. |
|
| (More Details) |
|
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Recommend reviewing statistical inference, linear regression model and generalized linear regression model; basic probability theory and common distribution; basic coding experience in R and/or other softwares such as Matlab, Python. |
| Requirements |
|
| Grading Method |
Student report about a relevant topic in Bayesian inference (deadline will be announced at last lecture). |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
|
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |