Data mining and machine learning study foundations and practical approaches for knowledge discovery from vast collections of complex data. This knowledge may come in the form of patterns, descriptive, predictive and prescriptive models.
This trajectory focuses on data mining and machine learning approaches and techniques for developing end- to-end solutions for algorithmic decision making that are so pervasive today that you probably use them dozens of times a day without knowing it, for instance in web search, speech recognition, and a variety of mobile phone applications. It is also a crucial component of data-driven industry, scientific discovery, and modern healthcare. One of the fascinating aspects of data mining and machine learning is that they automate the process by learning from examples rather than being explicitly programmed. We, as engineers, come up with approaches for meta-programming, that is, we develop intelligent computer programs that can learn to induce new useful programs from the training examples.
The trajectory contains the following courses:
- 2AMM20 - Research Topics in Data Mining,
- 2AMM15 - Machine Learning Engineering,
- 2AMM10 - Deep Learning,
- 2AMM30 - Text Mining, and
- 2AMS40 - Optimal Decision Making & Reinforcement Learning
You will study the theoretical foundations of data analysis and get in touch with the latest research topics and techniques in Data Mining (2AMM20). You will learn principles and engineering aspects of machine learning, from data collection to machine learning systems (2AMM15). You will learn how to build models for high- dimensional data without explicit feature engineering steps using artificial neural networks and deep learning and understand how those techniques are employed for learning representations (2AMM10).
For an overview of the full course list of this master program, visit this page.