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Quantitative Reasoning - Thinking in Numbers
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| Quantitative Reasoning Thinking in Numbers |
The many detailed problems and worked solutions in the text and extensive appendices help the reader learn mathematical areas such as algebra, functions, graphs, and probability. End-of-chapter problem material provides practice for students, and suggested projects are provided with each chapter. A solutions manual is available online for instructors.
- Accessible, engaging and self-contained - a one-stop shop for the reader to review or learn basic mathematical skills while learning how to apply them to multi-step arguments in a quantitative way
- Tailored for courses in quantitative reasoning and critical reasoning
- Assumes only high-school mathematics, accommodating a wide range of preparation
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Fat Chance - Probability from 0 to 1
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| Fat ChanceProbability from 0 to 1 |
This book establishes simple principles of counting collections and sequences of alternatives, and elaborates on these techniques to solve real world problems both inside and outside the casino. Pair this book with the HarvardX online course for great videos and interactive learning: https://harvardx.link/fat-chance.
- Highlights key definitions, formulas, and theorems in boxes for easy reference
- Some twenty-five essential formulas are conveniently collected in the back of the book
- More than 100 exercises and forty worked examples build up from simple problems to complex real-world problems
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Mathematics for Machine Learning
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| Mathematics for Machine Learning |
For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
- A one-stop presentation of all the mathematical background needed for machine learning
- Worked examples make it easier to understand the theory and build both practical experience and intuition
- Explains central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines
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Probability and Computing - Randomization and Probabilistic Techniques in Algorithms and Data Analysis
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| Probability and Computing Randomization and Probabilistic Techniques in Algorithms and Data Analysis |
Among the many new exercises and examples are programming-related exercises that provide students with excellent training in solving relevant problems. This book provides an indispensable teaching tool to accompany a one- or two-semester course for advanced undergraduate students in computer science and applied mathematics.
- Contains all the background in probability needed to understand many subdisciplines of computer science
- Includes new material relevant to machine learning and big data analysis, enabling students to learn new, up-to-date techniques and applications
- Newly added chapters and sections cover the normal distribution, sample complexity, VC dimension, naïve Bayes, cuckoo hashing, power laws, and the Lovasz Local Lemma
- Many new exercises and examples, including several new programming-related exercises, provide students with excellent training in problem solving
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Take a break, relax and have a cup of coffee!
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