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[null,"1.","Record Nr.",null,"UNINA9910150483603321"]
[null,null,"Autore",null,"Oakley Barbara <1949->"]
[null,null,"Titolo",null,"Mind for Numbers, A : How to Excel at Math and Science Even If You Flunked Algebra"]
[null,null,"Pubbl/distr/stampa",null,"Gildan Audio"]
[null,null,"ISBN",null,"1-4690-3083-7"]
[null,null,"Disciplina",null,"501/.9"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Musica"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","Whether you are a student struggling to fulfill a math or science requirement, or you are embarking on a career change that requires a higher level of math competency, A Mind for Numbers offers the tools you need to get a better grasp of that intimidating but inescapable field. Engineering professor Barbara Oakley knows firsthand how it feels to struggle with math. She flunked her way through high school math and science courses, before enlisting in the army immediately after graduation. When she saw how her lack of mathematical and technical savvy severely limited her options-both to rise in the military and to explore other careers-she returned to school with a newfound determination to re-tool her brain to master the very subjects that had given her so much trouble throughout her entire life. In A Mind for Numbers, Dr. Oakley lets us in on the secrets to effectively learning math and science-secrets that even dedicated and successful students wish they'd known earlier. Contrary to popular belief, math requires creative, as well as analytical, thinking. Most people think that there's only one way to do a problem, when in actuality, there are often a number of different solutions-you just need the creativity to see them. For example, there are more than three hundred different known proofs of the Pythagorean Theorem. In short, studying a problem in a laser-focused way until you reach a solution is not an effective way to learn math. Rather, it involves taking the time to step away from a problem and allow the more relaxed and creative part of the brain to take over. A Mind for Numbers shows us that we all have what it takes to excel in math, and learning it is not as painful as some might think!"]
[null,"2.","Record Nr.",null,"UNINA9910896193203321"]
[null,null,"Autore",null,"Picci Giorgio"]
[null,null,"Titolo",null,"An Introduction to Statistical Data Science : Theory and Models / / by Giorgio Picci"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"9783031666193"]
[{"$nulls":4},"3031666194"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (437 pages)"]
[null,null,"Disciplina",null,"519.5"]
[null,null,"Soggetti",null,"Statistics"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Engineering mathematics"]
[{"$nulls":4},"Artificial intelligence - Data processing"]
[{"$nulls":4},"Statistical Theory and Methods"]
[{"$nulls":4},"Bayesian Inference"]
[{"$nulls":4},"Statistical Learning"]
[{"$nulls":4},"Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences"]
[{"$nulls":4},"Engineering Mathematics"]
[{"$nulls":4},"Data Science"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"- 1. Introduction -- 2. Classical Statistical Inference -- 3. Linear Models -- 4. Conditioning and Regularization -- 5. Linear Hypotheses and LDA -- 6. Bayesian Statistics -- 7. Principal Component Analysis -- 8. Non Linear Inference -- 9. Time Series."]
[null,null,null,"Sommario/riassunto","This graduate textbook on the statistical approach to data science describes the basic ideas, scientific principles and common techniques for the extraction of mathematical models from observed data. Aimed at young scientists, and motivated by their scientific prospects, it provides first principle derivations of various algorithms and procedures, thereby supplying a solid background for their future specialization to diverse fields and applications. The beginning of the book presents the basics of statistical science, with an exposition on linear models. This is followed by an analysis of some numerical aspects and various regularization techniques, including LASSO, which "]
[{"$nulls":4},"are particularly important for large scale problems. Decision problems are studied both from the classical hypothesis testing perspective and, particularly, from a modern support-vector perspective, in the linear and non-linear context alike. Underlying the book is the Bayesian approach and the Bayesian interpretation of various algorithms and procedures. This is the key to principal components analysis and canonical correlation analysis, which are explained in detail. Following a chapter on nonlinear inference, including material on neural networks, the book concludes with a discussion on time series analysis and estimating their dynamic models. Featuring examples and exercises partially motivated by engineering applications, this book is intended for graduate students in applied mathematics and engineering with a general background in probability and linear algebra."]
[null,"3.","Record Nr.",null,"UNIORUON00429617"]
[null,null,"Autore",null,"LaVALLEY, Albert J."]
[null,null,"Titolo",null,"Carlyle and the idea of the modern : studies in Carlyle's prophetic literature and its relation to Blake, Nietsche, Marx and others / Albert J. LaValley"]
[null,null,"Pubbl/distr/stampa",null,"New Haven and London, : Yale university, 1968"]
[null,null,"Descrizione fisica",null,"x, 351 p. ; 23 cm."]
[null,null,"Disciplina",null,"820.09"]
[null,null,"Soggetti",null,"CARLYLE T."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]