At the end of the course you will be able to
- reason and argue which data mining algorithm is applicable to which task;
- apply, analyze, and implement various data mining algorithms;
- evaluate the quality of data mining solutions.
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How can we build systems that can learn? More specifically: how can we extract relevant, interesting information from (big) data? You learn that there are various algorithms, depending on the task at hand and properties of the available data. In the project, you will implement and/or test such algorithms on existing data.
Instructional Modes
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You
- are up-to-date with elementary concepts from probability theory such as probabilities, probability distributions, and expectations;
- can apply these concepts for basic calculations;
- know and understand vectors and matrices;
- can add and multiply those. This prior knowledge is treated in the courses Calculus and Probability Theory and Matrix Calculation
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Grading is based upon a midterm exam (35%), and endterm exam (35%), and a project (30%). Homework assignments are mandatory and a sufficient grade is needed to pass the course. A single resit exam replaces both midterm and endterm exams and then counts for 70%.
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