A hands-on textbook for advanced undergraduates and beginning graduate students in data science, offering an accessible introduction to the essential mathematics behind modern data analysis. With practical Python examples and hundreds of exercises and problems, it's an invaluable resource for students and educators alike.
A hands-on textbook for advanced undergraduates and beginning graduate students in data science, offering an accessible introduction to the essential mathematics behind modern data analysis. With practical Python examples and hundreds of exercises and problems, it's an invaluable resource for students and educators alike.
Sébastien Roch is a Vilas Distinguished Achievement Professor of Mathematics at the University of Wisconsin, Madison. At UW-Madison, he helped establish the Data Science Major and has developed several courses on the mathematics of data. He is the author of Modern Discrete Probability: An Essential Toolkit (2023).
Inhaltsangabe
1. Introduction: a first data science problem 2. Least squares: geometric, algebraic, and numerical aspects 3. Optimization theory and algorithms 4. Singular value decomposition 5. Spectral graph theory 6. Probabilistic models: from simple to complex 7. Random walks on graphs and Markov chains 8. Neural networks, backpropagation and stochastic gradient descent.
1. Introduction: a first data science problem 2. Least squares: geometric, algebraic, and numerical aspects 3. Optimization theory and algorithms 4. Singular value decomposition 5. Spectral graph theory 6. Probabilistic models: from simple to complex 7. Random walks on graphs and Markov chains 8. Neural networks, backpropagation and stochastic gradient descent.
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