Description
Observing the world and compressing these observations into compact rules have been of great importance to humankind for ages. Nowadays we collect and generate a lot of data, so big that no human can analyze it. Machine learning is a field of science that is responsible for designing computer algorithms capable of learning important patterns directly from the large volumes of data without being explicitly programmed to. In this course, we are going to look into the principles and techniques that are at the core of machine learning. Topics will include notions of supervised and unsupervised learning; classification, regression, clustering and dimensionality reduction methods; deceptive effects of overfitting and ways to estimate models’ generalization power. Separately, we will look into time series modeling.
Learning Objectives:
- Review different classes of Machine Learning methods;
- Learn details of inner workings of some of the most important Machine Learning methods;
- Learn pros and cons and potential application domain of each method;
- Learn most common problems that are encountered when training Machine Learning models and ways to prevent them;
- Learn the fundamental differences between time-series and other types of data;
- Learn modeling methods specific to time-series.
Learning outcomes:
At the end of this course, the student should be able to:
- Review different classes of Machine Learning methods;
- Learn details of inner workings of some of the most important Machine Learning methods;
- Learn pros and cons and potential application domain of each method;
- Learn most common problems that are encountered when training Machine Learning models and ways to prevent them;
- Learn the fundamental differences between time-series and other types of data;
- Learn modeling methods specific to time-series.
Course structure:
- Supervised Learning
- Unsupervised Learning
- Performance estimation
- Timeseries modeling and analysis
- Introduction to Deep Learning
- Applications and Ethics
Course prerequisites:
To make the learning process interactive and gained skills more practical we will implement many of the mentioned algorithms in Python using Google Colab as an interactive environment. Thus familiarity with Python and some of its libraries (numpy, matplotlib, scipy and pandas) is a prerequisite for this course. Also, students are expected to know the basics of calculus, linear algebra, and probability theory.
Exams & certification:
After the successful completion of the course, the participants will get a certificate.
Language: English
