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.

AM9624B / Psych 9221B: Introduction to Neural Networks (Mur / Muller)

Computational neuroscience is an exciting and developing field, with rapidly growing applications in technology and industry. Researchers in this field employ numerical modeling techniques and mathematical approaches to answer fundamental questions about neural structure, dynamics, and computation. This one-semester course will provide students with an introduction to computational neuroscience. Students will learn fundamentals of neural computation and explore how networks of neurons support information processing in the brain. Through this material, students will become familiarized with computational modeling, programming, and machine learning techniques.

CS9542B / CS4442: Artificial Intelligence II (Mohsenzadeh / Wang)

This course is a continuation of CS3346, Artificial Intelligence I. A broad range of areas falls into the field of Artificial Intelligence. This course introduces two very active areas of Artificial Intelligence: machine learning and computer vision, covering topics from early, to mid- and high-level vision, including basics of machine learning and deep convolutional neural networks for vision. The course covers both algorithmic perspectives of artificial intelligence and their practical applications in computer vision.