京都大学 京都大学

Statistical data analysis, Advanced A

Numbering Code G-ECON31 6A613 LJ44
G-ECON31 6A613 LJ43
G-ECON31 6A613 LJ10 		Year/Term 2022 ・ Second semester
Number of Credits 2 Course Type Lecture
Target Year Target Student
Language Japanese Day/Period Mon.2
Instructor name AKITA YUYA (Graduate School of Economics Professor)
Outline and Purpose of the Course Analyzing various data to extract meaningful and valuable information, and constructing models to represent and interpret data, are important techniques these days. Data is becoming bigger and bigger, thus efficient and effective ways of data processing are required. In this course, we will learn a variety of methods in information science for statistical analysis, classification, and prediction from data, i.e., pattern recognition and machine learning. We also exercise them by using the Python language.
Course Goals - Understanding the theoretical background and characteristics of methods for pattern recognition and machine learning.
- Learning processes of the methods in the Python language.
Schedule and Contents Generally, two or three lectures, including exercises with Python, will be conducted on each of the following topics. Depending on the progress of the class, the order of topics and the number of lectures may be changed.

Fundamentals of pattern recognition
Clustering
Machine learning methods
Neural networks
Text mining
Evaluation Methods and Policy Evaluation will be based on midterm and end-term reports (50 marks each).
Course Requirements Students should have taken "Statistical data analysis, Basic A." Understanding of statistics and linear algebra is expected. Students are also required to have the basic skills of the Python language, together with basic knowledge and skills to use computers.
Study outside of Class (preparation and review) Each class requires understanding of the contents of the previous lectures, thus review them thoroughly.
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