NEUS1300 Data Science and Advanced Python Concepts Workshop (1–10 cr)

The course focuses on hands-on data science with diverse dataset, including dataset from neuroscience,
cognitive science, vision, sound, language and text.
Students will review machine-learning related Python modules, and understand advanced Python
concepts. They will work to reproduce results that were published in a recent literature of the data
science or neuroscience community.

Students will obtain hands-on experience with building a data science pipeline, including collecting data,
data preprocessing, training a machine learning model, analyzing the results, and publishing open-
source code.

Learning outcomes:
1. Train and evaluate machine learning and deep learning models
2. Write clear and efficient code in Python
3. Demonstrate proficiency in fundamental concepts in machine learning, including supervised,
self-supervised and unsupervised learning
4. Apply data science and machine learning techniques to neuroscience-related data

Students must have working knowledge of Python programming language. This means they
have already completed a course where home assignments were done in Python.
2. Students should have completed, or currently participate in, a course in machine leaning. For
example, neural networks (27-504) or equivalent courses.

Machine Learning and Pattern recognition, C. Bishop (2006)
Deep Learning, I. Goodfellow and Y. Bengio (2015)
The Elements of Statistical Learning, T. Hastie et al. (2001)