Neural Tangent Kernel: Generalization in Deep Learning

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Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Linear Models: Recap and Logistic Regression

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Kernel Methods: SVM and Regression

Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.

Linear Models: Introduction

Introduces linear models, regression, Gaussian distribution, linearity, and model generalization.

Linear Models for Classification

Covers linear models for classification, logistic regression training, evaluation metrics, and decision boundaries.

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Linear Models for Classification

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Assessing Significance and Fit

Covers confidence intervals, R2, and examples on cement heat evolution and car horsepower-MPG relationships.