Data Characterization: PCA & Spike Sorting

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Page 3 of 4

Explores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.

Explores the evolution from data analysis to AI and ML, emphasizing big data, machine learning, and social media interaction.

Covers the process of EMG data analysis and signal inversion for further analysis.

Introduces the fundamentals of handling data, emphasizing the importance of Pandas and data modeling for effective analysis.

Covers the analysis of ECG signals related to hemodynamics.

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Explores heart rate variability analysis using ECG data and its physiological significance.

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.