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Statistical Analysis Advanced Course
Takafumi KANAMORI Associate Professor
Department: Graduate School of Information Science
|Class Time:||2016 Spring Monday|
|Recommended for:||Department of Computer Science and Mathematical Informatics|
- Review of probability theory: probability, conditional probability, expectation value, dispersion and other basics.
- Linear regression: least-square method, regularization, and cross-validation method.
- Kernel method: kernel regression analysis, and reproducing kernel Hilbert space.
- Discriminant analysis: Bayes' rule and bias-variance tradeoff.
- Support vector machine: algorithm, kernel SVM, and multi-classification.
In order for students to learn smoothly, all materials given in class will be available online. Also, submitted reports will be marked and returned in a week or so to promote self-study. There will be a specific emphasis on mathematical aspects in this class. The aim of this class is to be able to clearly express the train of thought used in information science, which is different from the conventional scientific paradigm.
To learn theoretical basics of machine learning and mathematical statistics.
None. Handouts will be provided.
Mohri, et al., Foundations of Machine Learning, The MIT Press, 2012.
|1||Introduction, review of probability and statistics|
|2||Linear regression: least-square method|
|3||Properties of least-square method and cross-validation method|
|4||High dimensional model and regularization|
|5||Kernel regression analysis|
|7||Reproducing kernel Hilbert space|
|10||Evaluation of prediction error and bias-variance tradeoff|
|12||Support vector machine (SVM): Algorithm|
|13||Support vector machine (SVM): Statistical property|
|14||Kernel SVM, multiple variable SVM|
|15||Multi-class classification based on error correcting output coding (ECOC) method|
Grades are based on the final exam and reports.
Page last updated May 23, 2018
The class contents were most recently updated on the date indicated. Please be aware that there may be some changes between the most recent year and the current page.