Kernel RegressionCovers the concept of kernel regression and making data linearly separable by adding features and using local methods.
Deep Learning FundamentalsIntroduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Support Vector MachinesIntroduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.
Multiclass ClassificationCovers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.
Deep Learning ParadigmExplores the deep learning paradigm, including challenges, neural networks, robustness, fairness, interpretability, and energy efficiency.
