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Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Explores neurophysiological data analysis, covering AP identification, firing rates, subthreshold activity, FFT spectral analysis, and event-triggered analysis using MATLAB.
Covers neurophysiological data analysis, including AP detection, firing rate computation, and spectral analysis, with a focus on predicting cell classes.
Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.
Explores linear models for classification, including parametric models, regression, and logistic regression, along with model evaluation metrics and maximum margin classifiers.
Covers linear regression basics, focusing on minimizing error using the principle of least squares and includes an ANOVA table and practical example in R.
