A diverse selection of data science topics explored through a mathematical lens.
"synopsis" may belong to another edition of this title.
Simon Foucart is Professor of Mathematics at Texas A&M University, where he was named Presidential Impact Fellow in 2019. He has previously written, together with Holger Rauhut, the influential book A Mathematical Introduction to Compressive Sensing (2013).
"About this title" may belong to another edition of this title.
FREE shipping within United Kingdom
Destination, rates & speeds£ 22.14 shipping from U.S.A. to United Kingdom
Destination, rates & speedsSeller: Toscana Books, AUSTIN, TX, U.S.A.
Hardcover. Condition: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks. Seller Inventory # Scanned1316518884
Quantity: 1 available
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New. Seller Inventory # 44152448-n
Quantity: Over 20 available
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Condition: New. In. Seller Inventory # ria9781316518885_new
Quantity: Over 20 available
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Hardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 650. Seller Inventory # C9781316518885
Quantity: Over 20 available
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Seller Inventory # 393200835
Quantity: 3 available
Seller: CitiRetail, Stevenage, United Kingdom
Hardcover. Condition: new. Hardcover. This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts. This text explores a diverse set of data science topics through a mathematical lens, helping mathematicians become acquainted with data science in general, and machine learning, optimal recovery, compressive sensing, optimization, and neural networks in particular. It will also be valuable to data scientists seeking mathematical sophistication. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781316518885
Quantity: 1 available
Seller: Revaluation Books, Exeter, United Kingdom
Hardcover. Condition: Brand New. 350 pages. 9.00x6.00x0.81 inches. In Stock. This item is printed on demand. Seller Inventory # __1316518884
Quantity: 1 available
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: As New. Unread book in perfect condition. Seller Inventory # 44152448
Quantity: Over 20 available
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New. Seller Inventory # 44152448-n
Quantity: Over 20 available
Seller: California Books, Miami, FL, U.S.A.
Condition: New. Seller Inventory # I-9781316518885
Quantity: Over 20 available