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 4Parametric probability density estimation
Session 5Linear regression model
Session 6Classification by generalized linear model 1
Session 7Classification by generalized linear model 2
Session 8Neural network
Session 9CNN, Kernel method: kernel function
Session 10SVM1
Session 11SVM2, Ensemble learning
Session 12Dimensionality reduction
Session 13Mixed Gaussian distribution and EM algorithm
Session 14Variational auto-encoder
＊＊This content is based on April 1, 2024. For the latest syllabus information and details, please check the syllabus information inquiry page provided by the university.＊＊