Data Science and Machine Learning 2: Tools

ECTS credits: 
Course Description: 

This course will build on the previous one (which introduced the basic concepts in machine learning) and will discuss state-of-the-art algorithms for supervised learning (linear models, lasso, decision trees, random forests, gradient boosting machines, neural networks, support vector machine, deep learning etc.). A large part of the course will be dedicated to using (hands-on) the software tools for machine learning used by data scientists in practice (various high-performance R packages, xgboost, libraries for deep learning etc.).

Learning Outcomes: 

Students will get a basic understanding of the main algorithms used in machine learning and will be able to use the main open source libraries implementing these algorithms in practical applications.



  • 45% Weekly Assignments (homework exercises). These will be submitted using Moodle.
  • 45% Final Exam (closed book)
  • 10% Quizzes at the beginning of each lecture, except the first lectures of each course. Missing a lecture or being late will result in 0% of the actual quiz score.

Weekly assignment acceptance policy and achievable grades:

  • 100% until the due date
  • 50% within 24 hours past the due date
  • 0% after that.

Grading policy

Students shall not miss more than 1 day of lectures/seminar (out of 8 days). Failing to do so will yield an administrative fail grade. (If you have a major impediment please contact the Instructor.) To pass, students will need to get at least 60% of the overall grade. Failure to do so, will yield a Fail grade.


Data Analysis 1,2,3 and Data Science and Machine Learning 1. (Concepts)

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