Convex Optimization for Machine Learning

ISBN-13: 9781638280521 / Angielski / Twarda / 2022 / 350 str.

South Korea) Suh Changho (KAIST

Spis treści

• Preface
• 1 Convex Optimization Basics
• 1.1 Overview of the book
• 1.2 Definition of convex optimization
• 1.3 Tractability of convex optimization and gradient descent
• 1.4 Linear Program
• 1.5 Least Squares
• 1.6 Test error, regularization and CVXPY implementation
• 1.7 Computed tomography
• 1.8 Quadratic program
• 1.9 Second-order cone program
• 1.10 Semi-definite program
• 1.11 SDP relaxation
• 1.12 Problem Sets
• 2 Duality
• 2.1 Strong duality
• 2.2 Interior point method
• 2.3 Proof of strong duality theorem
• 2.4 Weak duality
• 2.5 Lagrange relaxation for Boolean problems
• 2.6 Lagrange relaxation for the MAXCUT problem
• 2.7 Problem Sets
• 3 Machine Learning Applications
• 3.1 Supervised learning and optimization
• 3.2 Logistic regression
• 3.3 Deep learning
• 3.4 Deep learning II
• 3.5 DL: TensorFlow implementation
• 3.6 Unsupervised Learning: Generative modeling
• 3.7 Generative Adversarial Networks (GANs)
• 3.8 GANs: TensorFlow implementation
• 3.9 Wasserstein GAN
• 3.10 Wasserstein GAN II
• 3.11 Wasserstein GAN: TensorFlow implementation
• 3.12 Fair machine learning
• 3.13 A fair classifier and its connection to GANs
• 3.14 A fair classifier: TensorFlow implementation
• Appendices