Title of the course: Machine Learning I: Classification
Instructor: Asst. Prof. Kerem Altun
Institution: Işık Ü.
Dates: 14-20 August 2017
Prerequisites: Basic probability theory and linear algebra.
Level: Advanced undergraduate, beginning undergraduate
Abstract: This introductory machine learning course is aimed at undergraduate mathematics students and it focuses on pattern classification. We will discuss selected topics from the following through various applications:
Bayesian decision theory; Gaussian distribution and its properties; generative models; parameter estimation; the expectation-maximization algorithm; kernel density estimation
Discriminative models; (generalized) linear discriminants; support vector machines; the kernel trick
Dimensionality reduction; principal components analysis
Textbook or/and course webpage:
R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, 2nd edition, John Wiley & Sons, Inc., 2000.
E. Alpaydın, Yapay Öğrenme, 2. Basım, Boğaziçi Üniversitesi Yayınevi, 2013.
Language: TR, EN