Data Science and Machine Learning 2: Tools
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.).
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.
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)