Covers information measures like entropy, joint entropy, and mutual information in information theory and data processing.
Explores kernels for simplifying data representation and making it linearly separable in feature spaces, including popular functions and practical exercises.
Explores the ROC curve, True Positive Rate, False Positive Rate, and prediction probabilities in classification models.
Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.
