Gradient Descent: Optimization and Regularization

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Optimality of Convergence Rates: Accelerated/Stochastic Gradient Descent

Covers the optimality of convergence rates in accelerated and stochastic gradient descent methods for non-convex optimization problems.

Polynomial Regression and Gradient Descent

Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.

Structures in Non-Convex Optimization

Covers non-convex optimization, deep learning training problems, stochastic gradient descent, adaptive methods, and neural network architectures.

Simplex Algorithm: Basics

Introduces the Simplex algorithm for solving flow problems and handling negative cost cycles.

Suspension Optimisation: Performance Analysis and Optimization

Explores the design and optimization of car suspensions for improved performance.

Proximal and Subgradient Descent: Optimization Techniques

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

Differentiable Ranking and Sorting

Explores differentiable ranking and sorting techniques for machine learning applications.

Understanding Generalization: Implicit Bias & Optimization

Explores the trade-off between complexity and risk in machine learning models, the benefits of overparametrization, and the implicit bias of optimization algorithms.

Matrix Factorization: Optimization and Evaluation

Explores matrix factorization optimization, evaluation methods, and challenges in recommendation systems.

Deep Learning Building Blocks

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.