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%), an endterm exam (35%), and a project (30%). Homework assignments are mandatory and a sufficient grade is needed to pass the course.
As indicated in the Rules and Regulations, the weighted average of midterm and endterm exam must be a minimum of 5.0. The resit of midterm and endterm will both be scheduled in Q3.
Please note: If you take a resit of one of the exam activities, you must register for that particular resit exam as well as for the resit of the final grade. The latter is necessary so the course examiner can register your new grade.
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Please note: If you take a resit of one of the exam activities, you must register for that particular resit exam as well as for the resit of the final grade. The latter is necessary for the course examiner to register your new grade.
The full study load is associated with the final grade, and this is used by OSIRIS in the context of calculating the judicium.
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