|Lecturer||Takafumi KANAMORI, Associate Professor|
|Department||School of Informatics and Sciences, 2011 Fall|
|Recommended for:||School of Infomatics and Science (2・One session / week 15 weeks / semester)|
Lecture notes and assignments are uploaded on the web-site. While the main focus of the course is to introduce the mathematical basis of statistics, some examples using real-world data will also be shown.
To understand the mathematical foundations of statistics and various applications of data analysis.
Prior basic knowledge of analytics, linear algebra and probability is recommended.
No textbooks are assigned.
|2||Elements of probability I|
|3||Elements of probability II|
|4||Statistical inference I (unbiased estimator)|
|5||Statistical inference II (Fisher information)|
|6||Statistical inference III (Cramer-Rao inequality)|
|7||Confidence interval I (estimation with confidence)|
|8||Confidence interval II (confidence interval and the t-distribution)|
|9||Hypothesis Testing I (null hypothesis and alternative hypothesis)|
|10||Hypothesis testing II (errors in statistical test)|
|11||Hypothesis testing III (optimality, Neyman-Pearson lemma)|
|12||Linear regression I (inference of regression function)|
|13||Linear regression II (confidence interval and statistical test in linear regression)|
|14||Maximum likelihood estimator I (maximum likelihood estimator)|
|15||Maximum likelihood estimator II (exponential family)|
Grading will be based on homework assignments.
January 14, 2020