## Statistical Learning IIBack

Numbering Code | G-MED41 8S012 LE87 | Year/Term | 2021 ・ Intensive, Second semester | |
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Number of Credits | 2 | Course Type | lecture and seminar | |

Target Year | Doctoral students | Target Student | ||

Language | English | Day/Period | Intensive | |

Instructor name | YAMADA RYO (Graduate School of Medicine Professor) | |||

Outline and Purpose of the Course |
Data analysis methods in life science might be divided into two; (1) statistical tests and estimation and (2) machine learning. However the distinction between them is becoming vaguer and they are fusing together along with the change of data that get bigger and more complicated. The topics in the Statistical Learning I and II are the methods in machine learning field. The various methods are divided into 4 modules; Statistical Learning Basics A Statistical Learning Basics B Statistical Learning Application A Statistical Learning Application B Basics A and B focus on the ideas of learning approaches. Applications A and B focus on the skills of machine learning using Python. The schedule of modules are shown below. 2018 1st semester Basics A, 2nd semester Application B 2019 1st semester Basics B, 2nd semester Application A 2020 1st semester Basics A, 2nd semester Application B 2021 1st semester Basics B, 2nd semester Application A This semester is APPLICATION B. |
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Course Goals |
Depending of the contents of modules, the goal is: To be able to describe what statistical learning is. To understand supervised/non-supervised learning. To understand model selection. To understand regularization. To understand the basics of tree/neural network/support vector machine. To understand regression/classification/prediction of statistical learning. To be able to use Python's basic libraries for machine learning. |
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Schedule and Contents |
The order of topics is as below. Basics A: Orientation Supervised learning Linear method for regression Linear method for classification Basis expansion and regularization Kernel smoothing Basics B: Orientation Tree Neural network Support vector machine Application A: Introduction to Python Supervised learning (classificatio and regression) Non-supervised learning (Dimension reduction and feature extraction, clustering) Application B: Introduction to Python Decision tree Cross validation |
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Evaluation Methods and Policy | Activities in the class hours, homeworks and exam at the end of the course are count. | |||

Course Requirements | Register both the first and second semester classes, if possible. | |||

Study outside of Class (preparation and review) |
Needs preparation for presentation in the class hours. Needs to report to the homework tasks. |
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Textbooks | Textbooks/References |
Basics A,B 統計的学習の基礎―データマイニング・推論・予測― ISBN 9784320123625 The Elements of Statistical Learning ISBN 978-0387848570 |