Martin Andersen

Anders Hansson

Optimization for Learning and Control Comprehensive resource providing a masters’ level introduction to optimization theory and algorithms for learning and control Optimization for Learning and Control describes how optimization is used in these domains, giving a thorough introduction to both unsupervised learning, supervised learning, and reinforcement learning, with an emphasis on optimization methods for large-scale learning and control problems. Several applications areas are also discussed, including signal processing, system identification, optimal control, and machine learning. Today, most of the material on the optimization aspects of deep learning that is accessible for students at a Masters’ level is focused on surface-level computer programming; deeper knowledge about the optimization methods and the trade-offs that are behind these methods is not provided. The objective of this book is to make this scattered knowledge, currently mainly available in publications in academic journals, accessible for Masters’ students in a coherent way. The focus is on basic algorithmic principles and trade-offs. Optimization for Learning and Control covers sample topics such as: Optimization theory and optimization methods, covering classes of optimization problems like least squares problems, quadratic problems, conic optimization problems and rank optimization. First-order methods, second-order methods, variable metric methods, and methods for nonlinear least squares problems. Stochastic optimization methods, augmented Lagrangian methods, interior-point methods, and conic optimization methods. Dynamic programming for solving optimal control problems and its generalization to reinforcement learning. How optimization theory is used to develop theory and tools of statistics and learning, e.g., the maximum likelihood method, expectation maximization, k-means clustering, and support vector machines. How calculus of variations is used in optimal control and for deriving the family of exponential distributions. Optimization for Learning and Control is an ideal resource on the subject for scientists and engineers learning about which optimization methods are useful for learning and control problems; the text will also appeal to industry professionals using machine learning for different practical applications.

Optimization for Learning and Control

Mora Tuffy

Finalist Pacific Book Awards 2018! A celebration of one of the most simple joys in life: the unwitting comedy of children. Is there anything more likely to make you laugh and put a smile on your face than the innocent and funny remark of a child? Ever since Art Linkletter first published Kids Say the Darndest Things in 1957, people have laughed and been delighted with the things kids say. The Danes have a word for the feeling of happy delight that you get from simple things in life: “hygge.” In I’m So Full of Happy Today: The Hygge Wisdom of Children you will explore many of the hilarious things kids say in our world today, like these quotes: Dylan, age 5, “Mom, did it hurt much when you gave birth to Daddy?” Keira, age 8, “Being in love is like having a birthday in your stomach.” Miley, age 3, “When I grow up, I want to be pregnant. Or a princess.” Jack, age 5, “Mom, how old were you when you were my age?” The latest in funny kids books: I’m So Full of Happy Today is a heartwarming collection of the remarkably observant, funny, and wise things said by children, aged two to ten years. All of the stories have a very real and personal feel, and include the name and age of each child. These playful, sometimes “tongue-in-cheek” tales straight from the minds and mouths of kids will have you learning and laughing long after you’ve turned the last page. You will be hygge happy!

Martin Andersen

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