Machine Learning I

Course Purpose
Students learn the basics of pattern recognition and machine learning, such as neural networks, clustering, discriminant functions, linear regression, generalized linear models, and Bayesian inference, on the basis of calculus and linear algebra, probability, and statistics.
Learning Goals
Students obtain the concepts of the pattern recognition and machine learning, such as neural networks, clustering, discriminant functions, linear regression, generalized linear models, and Bayesian inference.
Topic
Session 1Introduction to machine learning
Session 2Probability / Statistics digest 1
Session 3Probability / Statistics digest 2
Session 4Maximum likelihood estimation and error functions / Evaluation indices
Session 5Parametric probability density estimation
Session 6Linear regression model
Session 7Classification by generalized linear models 1
Session 8Classification by generalized linear models 2
Session 9Neural networks
Session 10CNN
Session 11Principal component analysis
Session 12KL Divergence and t-SNE
Session 13Mixed Gaussian distribution and EM algorithm
Session 14Comprehensive exercise
＊＊This content is based on April 1, 2025. For the latest syllabus information and details, please check the syllabus information inquiry page provided by the university.＊＊