Numbering Code	U-ENG29 39122 LJ10 U-ENG29 39122 LJ12	Year/Term	2022 ・ Second semester
Number of Credits	2	Course Type	Lecture
Target Year		Target Student
Language	Japanese	Day/Period	Wed.2
Instructor name	KAWAHARA TATSUYA (Graduate School of Informatics Professor)
Outline and Purpose of the Course	This course provides foundations of modeling and systems, which extract useful information for classification and prediction from real-world data. It covers a variety of machine learning techniques oriented for pattern recognition.
Course Goals	to master basic approaches and major techniques of machine learning. to be able to design a system for pattern classification and recognition.
Schedule and Contents	1. Introduction to pattern recognition 2. Discriminant function and machine capacity 3. Discriminant function based on Gaussian distribution 4. Clustering and Gaussian mixture model 5. DP matching and HMM (classification of sequential patterns) 6. Bayes classification 7. Naive Bayes classifier and logistic regression model 8. Perceptron learning of discriminant function 9. Neural network 10. Support vector machines (SVM) 11. Statistical feature extraction 12. Maximum likelihood estimation and regularization 13. Deep learning(1) 14. Deep learning(2); Pattern recognition systems 15. Examination and Feedback
Evaluation Methods and Policy	The grading is based on the examination following the course, and some exercises provided in the course.
Course Requirements	None
Study outside of Class (preparation and review)	Excersize included in lecture slides
Textbooks	Textbooks/References	Lecture slides are provided via PandA CMS.