Neural Networks OptimizationExplores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.
Nonlinear ML AlgorithmsIntroduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Deep Learning ParadigmExplores the deep learning paradigm, including challenges, neural networks, robustness, fairness, interpretability, and energy efficiency.
Deep Learning: Theory and PracticeBy Prof. Volkan Cevher delves into the mathematics of deep learning, exploring model complexity, risk trade-offs, and the generalization mystery.
Convolutional Neural NetworksIntroduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.
Perception: Data-Driven ApproachesExplores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Deep Learning: Theory and ApplicationsExplores the mathematics of deep learning, neural networks, and their applications in computer vision tasks, addressing challenges and the need for robustness.
Generalization TheoryExplores generalization theory in machine learning, addressing challenges in higher-dimensional spaces and the bias-variance tradeoff.