Courses and Syllabi
The Computational Brain Science group is actively developing a new curriculum in Computational Neuroscience and Data Science in general. Here a list of relevant classes that are currently being taught:
DS2000B: Introduction to Data Science I (Diedrichsen)
The course covers three basic concepts of data science together with the corresponding techniques. (a) Sampling to estimate properties of a population (Bootstrap), (b) Random assignment and experiments to make causal inferences (Randomization test), (c) Model selection to enable good predictions (Cross-validation). The course covers practical data handling and programming skills in Python. It provides an ideal preparation for DS3000/ 90000.
DS9000A/B, DS3000 A/B: Introduction to Machine Learning (Diedrichsen)
This class covers the basic principles of Machine Learning. It is designed for students from various backgrounds, but requites some programming knowledge in Python, basic probability, Linear Algebra, and some calculus. It covers prediction and classification, model selection, and introduces a range of basic machine learning techniques. The class is being taught twice a year, both in the fall (A) and winter term (B). The class has a 2-hr lecture that covers the basic principles, and a 2-hr tutorial that shows how to implement the corresponding techniques in Python.