Cross-listed with PSYC489C and BSCI439Z. Credit only granted for PSYC489C, BSCI439Z, or NEUR338A.

Prerequisite: Minimum grade of C- in DATA120, and a minimum grade of C- in NEUR200 or BSCI353, or a higher-level Neuroscience course and Python experience (or permission of the instructor).

Analyze neuroscience data and integrate machine learning and AI techniques to probe cognition. This is an interdisciplinary course implementing modern data analysis techniques - from signal processing and classical machine learning to artificial neural networks - using Python programming to answer questions in neuroscience. Students will work withopen-source datasets, including EEG recordings of the Flanker task and FMRI data from the ABIDE dataset, with particular attention to behavioral analysis, brain decoding, and classifying neurodiversity. Ethics and course of bias will be considered throughout the course at all stages: data collection, analysis, model training, and interpretation.