Foundations of Statistical Modeling
| Numbering Code | U-ENG29 39136 LJ10 | Year/Term | 2022 ・ First semester | |
|---|---|---|---|---|
| Number of Credits | 2 | Course Type | Lecture | |
| Target Year | Target Student | |||
| Language | Japanese | Day/Period | Wed.4 | |
| Instructor name | KASHIMA HISASHI (Graduate School of Informatics Professor) | |||
| Outline and Purpose of the Course | This course gives foundations of statistical data modeling methods to capture the uncertainty in target systems and to estimate the probability of future events for prediction and control. | |||
| Course Goals | The goal of this course is to learn how to choose and apply appropriate processing and modeling approaches to analyze various types of data. | |||
| Schedule and Contents |
Basic ideas,1time,Basic ideas of statistical data analysis ,1time, Regression models,1time,Linear regression model and estimation methods Model estimation,2times,Model estimation frameworks including maximum likelihood estimation Model selection,2times,Model selection frameworks including information criterion Models for categorical data,2times,Predictive models for categorical data including logistic regression Correlation and causation,2times,Difference between correlation and causation. Methods for estimating causality. Bayesian estimation,2times,Statistical inference methods based on Bayesian statistics Models for various data types,2times,Models for various data types including time series and texts |
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| Evaluation Methods and Policy | Mid-term and final examinations | |||
| Course Requirements | Basic knowledge of probability and statistics | |||
| Study outside of Class (preparation and review) | Exercises on real data analysis. | |||
| Textbooks | Textbooks/References | None | ||
| References, etc. | They will be given in the lectures | |||
| Courses delivered by Instructors with Practical Work Experience |
分類： A course with practical content delivered by instructors with practical work experience |
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| Related URL | ||||
