Machine learning is the practice of applying algorithmic models to data, in an iterative manner, so that your computer discovers hidden patterns or trends that you can use to make predictions. This course covers the basic concepts and techniques of machine learning from both theoretical and practical perspectives. We will look at many different machine learning algorithms for regression, classification, clustering, neural networks, and a touch of deep learning among others. Students are expected to find ways to apply machine learning in the real world.
Applied Data Science aims to achieve two main goals. The first is to optimize the efficiency of decision making by human managers. The second is to maximize the utilization of available data, so that no important clue is ever missed. This course aims at building expertise required to achieve those goals in practice. Students will have the opportunity to gain and solidify knowledge of the most important contemporary methods of Data Science, and to develop understanding of practical applicability of the studied topics in business scenarios. They will be able to learn how to formulate analytic tasks in support of business objectives, how to define successful analytic projects, and how to evaluate utility of existing and potential applications of the discussed technologies in practice.