AS-008 Mathematical Models and Partial Differential Equations

Professor
Prof. Enrico Valdinoci from the University of Western Australia


AS-010 Statistical Decision Making

Statistical Decision Making introduces students to various statistical techniques supporting the study of computing and science. The course will present and explain the correct principles and procedures for collecting and analyzing data, as well as provide an introduction to data analysis using the open-source statistics software R / RStudio. Topics include describing different sets of data, probability distributions, statistical inference, bootstrapping, hypothesis tests, and simple linear regression and correlation.

Course syllabus.

Professor
A/Prof. Volker Gebhardt, Western Sydney University, Australia

AS-009 Poetry of Programming - Puzzle Based Introduction to Functional Programming

This is a Clojure programming course designed for non-programmers, in particular for Liberal Arts students with some college/high school algebra background. The course discusses the functional core of the language. It is designed for experiencing the joy of computer programming, seeing mathematical ideas in use, and getting fundamental and powerful programming skills, that could kick-start a serious IT career.

Course syllabus.

Professor

Dr. Attila Egri-Nagy, Akita International University, Japan

Personal webpage: https://dbsg.aiu.ac.jp/html/100000183_en.html

AS-002 Statistical Learning

This course provides a graduate level account of the field of machine learning, with a specific focus on applications.The preferred software environments for the implementation of statistical computing and graphics in this module are R and Python.

Course syllabus.

Professor
Dr. Eugene Kashdan, University College Dublin, Ireland

AS-005 Linear Algebra and Differential Equations

Elementary ordinary differential equations, linear systems of equations, Gaussian elimination, matrices and matrix operations, determinants, eigenvalues and eigenvectors, linear systems of ordinary differential equations, nonlinear systems, and stability.

Course prerequisites: a calculus course including multivariate calculus.

Course syllabus.

Professor
Dr. Peter Hinow, University of Wisconsin - Milwaukee, USA


AS-011 Introduction to Machine Learning

Machine learning is the science of getting computers to act without being explicitly programmed. Machine learning was at the core of the most successful companies of the past twenty years, and has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. In this class, students will learn about the most common machine learning techniques, and gain practice implementing them and getting them to work in the programming language Python. The class will also introduce concepts of big data analysis.

This introductory course gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as the data mining process, training and testing classification and clustering and ending up with more recent topics such as boosting, random forests and big data. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. By the end of class, students are expected to accomplish a project related to machine learning.

Course syllabus

Professor

Prof. Florent Domenach, Director of the Global Connectivity Program from Akita International University, Japan

Personal webpage: https://dbsg.aiu.ac.jp/html/100000182_en.html