Data Science and Machine Learning 1: Concepts
Type: core for MSc in Business analytics
After an overview of the entire data science landscape, this course will focus on machine learning. The course will introduce the main fundamental concepts in machine learning (supervised learning, training, scoring, accuracy measures, test set, overfitting, cross-validation, model capacity, hyperparameter tuning, grid and random search, regularization, ensembles, model selection etc.) The concepts will be illustrated with R code, therefore, it requires prior familiarity with R.
Students will get a basic understanding of the main concepts in machine learning. They will be prepared for the next course Data Science and Machine Learning 2 (Tools).
- 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 lecture. Missing a lecture or being late will result in 0% of the actual quiz score.
Weekly assignment dates and deadlines:
- Jan 16, due on Jan 22 8:00 AM
- Jan 23, due on Jan 29 8:00 AM
- Jan 30, due on Feb 5 8:00 AM
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 and 1 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