Adversarial Machine Learning: Fundamentals and Techniques

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Structures in Non-Convex Optimization

Delves into structures in non-convex optimization, emphasizing scalable optimization for deep learning.

Optimization Methods: Convergence and Trade-offs

Covers optimization methods, convergence guarantees, trade-offs, and variance reduction techniques in numerical optimization.

Proximal Operators: Optimization Methods

Explores proximal operators, subgradient methods, and composite minimization in optimization.

Linear Models and Overfitting

Explores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Linear Models for Classification

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

Machine Learning and Modern AI: SWOT Analysis

Covers a SWOT analysis of Machine Learning and Artificial Intelligence, exploring strengths, weaknesses, opportunities, and threats in the field.

Linear Models: Continued

Explores linear models, regression, multi-output prediction, classification, non-linearity, and gradient-based optimization.

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Proximal and Subgradient Descent: Optimization Techniques

Discusses proximal and subgradient descent methods for optimization in machine learning.