This concise volume seeks to address the silent void that university students embarking on mathematical coursework inevitably face: How does one apply ideas, concepts, and theory to story problems that at first seem intractable? We pay equal attention to the abstract aspects of problem solving as well as the practical. We discuss common misperceptions of ability and pay close attention to the attitudes and habits employed by successful problem solvers. We reveal problem solving as both achievable and fun, and illustrate these themes with specific examples and stories throughout the text. We also enumerate simple, executable steps that can be implemented when faced with virtually any new problem: understand the problem; draw a picture; define variables; be consistent; utilize principles. Far from trivial, these steps often lead novice problem solvers to error. In sum, we hope you find this charming work to be an indispensable guide on your journey and an effective means to improving and maturing your problem-solving capabilities.
This text introduces linear algebra--boiled to its essence--presented in a clear and concise fashion. Designed around a single-semester undergraduate course, Essential Linear Algebra introduces key concepts, various real-world applications, and provides detailed yet understandable proofs of key results that are aimed towards students with no advanced preparation in proof writing. The level of sophistication gradually increases from beginning to end in order to prepare students for subsequent studies.We begin with a detailed introduction to systems of linear equations and elementary row operations. We then advance to a discussion of linear transformations, which provide a second, more geometric, interpretation of the operation of matrix-vector product. We go on to introduce vector spaces and their subspaces, the image and kernel of a transformation, and change of coordinates. Following, we discuss matrices of orthogonal projections and orthogonal matrices. Our penultimate chapter is devoted to the theory of determinants, which are presented, first, in terms of area and volume expansion factors of 2x2 and 3x3 matrices, respectively. We use a geometric understanding of volume in n-dimensions to introduce general determinants axiomatically as multilinear, antisymmetric mappings, and prove existence and uniqueness. Our final chapter is devoted to the theory of eigenvalues and eigenvectors. We conclude with a number of discussions on various types of diagonalization: real, complex, and orthogonal.
In this series, we cover statistical experimentation, modeling, and machine learning, focusing on:basic statistics, hypothesis testing, and experimentationvalidation methodologylinear and logistic regression; generalized linear modelscausalitytime series analysisBayesian statisticsclusteringdecision trees, random forests, and boosted forestsartificial neural networks and deep learningreinforcement learningOur focus is on mathematical derivations, algorithmic development, and programming. We use Python throughout the text to support our theory. All machine learning techniques are coded from scratch using Python.
In Part III of this series, we cover the fundamentals of machine learning, focusing on:validation methodology (reprint)nearest neighbor, k-means, support vector machines, principal component analysistree-based methods: decision trees, bagging, random forest, boosting, XGBoostartificial neural networks and deep learningreinforcement learningThe focus is on algorithmic development and programming. We code each technique from scratch in Python, using an object-oriented approach.
This concise volume seeks to address the silent void that university students embarking on mathematical coursework inevitably face: How does one apply ideas, concepts, and theory to story problems that at first seem intractable? We pay equal attention to the abstract aspects of problem solving as well as the practical. We discuss common misperceptions of ability and pay close attention to the attitudes and habits employed by successful problem solvers. We reveal problem solving as both achievable and fun, and illustrate these themes with specific examples and stories throughout the text. We also enumerate simple, executable steps that can be implemented when faced with virtually any new problem: understand the problem; draw a picture; define variables; be consistent; utilize principles. Far from trivial, these steps often lead novice problem solvers to error. In sum, we hope you find this charming work to be an indispensable guide on your journey and an effective means to improving and maturing your problem-solving capabilities.
In Part II of this series, we cover the elements of statistical modeling, focusing on: validation methodology principles of object-oriented design linear and logistic regression generalized linear models causality time series analysis Bayesian statistics, including simulations in pymc3 Modeling customer lifetime values, including a detailed study of the beta-Bernoulli/beta-binomial model, a discretized version of the classic Pareto/NBD an introduction to credibility theory The theory is illustrated with simulations in Python throughout the text.
This concise volume seeks to address the silent void that university students embarking on mathematical coursework inevitably face: How does one apply ideas, concepts, and theory to story problems that at first seem intractable? We pay equal attention to the abstract aspects of problem solving as well as the practical. We discuss common misperceptions of ability and pay close attention to the attitudes and habits employed by successful problem solvers. We reveal problem solving as both achievable and fun, and illustrate these themes with specific examples and stories throughout the text. We also enumerate simple, executable steps that can be implemented when faced with virtually any new problem: understand the problem; draw a picture; define variables; be consistent; utilize principles. Far from trivial, these steps often lead novice problem solvers to err. In aggregate, we hope you find this charming work to be an indispensable guide on your journey and an effective means to improving and maturing your problem-solving capabilities.
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