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OVERVIEW

This workshop is aimed to students and researchers aiming to understand the basic principles of machine learning. It will focus on supervised learning, starting with linear models (regression, logistic regression, support vector machines) and will extend to the basic technologies of deep learning and kernel methods for vector data, signals, and structured data. Basic principles of learning theory that are useful to analyze results of practical applications will be also covered. Finally, there will be practical sessions using scikit-learn, TensorFlow, and Keras. After completing the workshop, students should able to understand the most popular learning algorithms, to apply them to solve simple practical problems, and to analyze and interpret the results. All course materials (including copies of presentations, practical exercises, data files, and example scripts prepared by the instructing team) will be provided electronically to participants.

TARGETED AUDIENCE & ASSUMED BACKGROUND

This workshop is aimed at all researchers and technical workers with a background in biology, computer science, mathematics, physics or related disciplines who want to understand and apply supervised machine learning algorithms to practical problems. The syllabus has been planned for people with zero or very basic knowledge of machine learning.

Students are assumed to know calculus, linear algebra, and algorithms and data structures at the undergraduate level. Students should also have sufficient programming skills, and preferably previous knowledge of the Python programming language.

FOR MORE INFORMATION

Session content: https://www.physalia-courses.org/courses-workshops/course43/curriculum43/
For more information about the course, please visit our website: https://www.physalia-courses.org/courses-workshops/course43/
Here is the full list of our courses and Workshops: https://www.physalia-courses.org/courses-workshops/

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