Machine Learning

The objective of the Machine Learning course is to provide the basic skills to analyze real problems, identify proper solutions for knowledge discovery, and design/implement analytic models. The course presents the best practices from both a theoretical and practical perspective with special focus on Human Centered application problems.
The course covers numerous well-studied methods such as classification, regression, structured prediction, clustering, and representation learning. It familiarizes the students with popular techniques for pattern recognition, knowledge discovery, and data analysis/modeling using Python, the most common language in the field.

At the end of the course students will be able to understand and discuss the principles of machine learning. They will be able to analyze practical problems, and to design and implement suitable solutions. They will be familiar with the most important techniques in the field and will be able to use them to build effective machine learning systems.
The students will be able to:
- Understand the broad applications of machine learning across various societal contexts
- Develop a comprehensive understanding of ML concepts and identify the best models to fit various applications
- Integrate multiple data management techniques: data preprocessing, learning, regularization, and model selection
- Develop and implement machine learning algorithms and devise solutions to real-life problems within human centric domains
- Describe the properties of models and algorithms for learning and explain the practical implications of the results
- Collaborate effectively on machine learning projects and assignments with both experts and peers.