Course ObjectiveThere are several learning objectives for this course. After completion
of this course, the student should be able to:
1. understand the capabilities and the limitations of machine learning,
2. implement machine learning algorithms in Python,
3. know relevant machine learning algorithms for both supervised and
unsupervised learning problems,
4. select the right machine learning models for real-world use cases,
5. understand when to apply online learning, reinforcement learning, and
6. interpret the outcomes of machine learning algorithms.
Course ContentMachine learning is the science of getting computers to act without
being explicitly programmed. Machine learning is so pervasive today that
it is used in everyday life without knowing it. In this course, you will
learn about the most effective machine learning techniques, and gain
practice implementing them and getting them to work yourself. We will
discuss the theoretical underpinnings as well as the practical know-how
needed to apply these techniques to new problems.
Teaching MethodsLectures (14 x 2 hours) including guest speakers, and tutorials (7 x 2
Method of AssessmentTutorial and programming assignments (20% of the final grade) and a
written exam (80% of the final grade). Both parts have to be passed with
at least a 5.5.
Entry RequirementsThe VU course Linear Algebra and the VU course Statistics, or equivalent
LiteratureSlides and additional material that will be posted on Canvas.
Target AudiencemBA, mBA-D, mMath, mSFM, mCS
|Language of Tuition||English|
|Faculty||Faculty of Science|
|Course Coordinator||prof. dr. S. Bhulai|
|Examiner||prof. dr. S. Bhulai|
prof. dr. S. Bhulai
You need to register for this course yourself
Last-minute registration is available for this course.
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