Help students raise their performance on the Regents Algebra I (Common Core) exam with NYS Finish Line Algebra I. Nearly 300 pages of practice can prepare them with CCLS instruction that follows the curriculum sequence outlined by New York State. Content and organization are developed especially for New York. Topics that are often stumbling blocks for students are covered in detail, starting with the fundamentals. The progression of skills goes from recognizing and understanding forms and processes, to solving equations and inequalities, to modeling equations and graphs to represent real-life situations. Rigorous multiple-choice and constructed-response items give students test-like practice.

Prepare students for Pennsylvania's end-of-course assessment with Keystone Finish Line Literature. Lessons are aligned to the Keystone Exams: Literature Assessment Anchors and Eligible Content, and provide plenty of practice with the types and length of literature found on the test. The book is divided into two modules with a review at the end of each: Module 1 focuses on fiction, such as plays, poems, short stories, and classic literature; Module 2 covers nonfiction, such as functional, instructional, expository, and argumentative texts. Just like the Keystone, many practice questions feature authentic texts with items that address Depth of Knowledge (DOK) levels 2 and higher and students will answer multiple-choice and constructed-response questions. Each lesson is specific to a skill or content area, and includes an instructional review, guided practice, and independent work.

Accessible but rigorous, this outstanding text encompasses all of the topics covered by a typical course in elementary abstract algebra. Its easy-to-read treatment offers an intuitive approach, featuring informal discussions followed by thematically arranged exercises. This second edition features additional exercises to improve student familiarity with applications. 1990 edition.

A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.

Why so many of America's public university students are not graduating—and what to do about it The United States has long been a model for accessible, affordable education, as exemplified by the country's public universities. And yet less than 60 percent of the students entering American universities today are graduating. Why is this happening, and what can be done? Crossing the Finish Line provides the most detailed exploration ever of college completion at America's public universities. This groundbreaking book sheds light on such serious issues as dropout rates linked to race, gender, and socioeconomic status. Probing graduation rates at twenty-one flagship public universities and four statewide systems of public higher education, the authors focus on the progress of students in the entering class of 1999—from entry to graduation, transfer, or withdrawal. They examine the effects of parental education, family income, race and gender, high school grades, test scores, financial aid, and characteristics of universities attended (especially their selectivity). The conclusions are compelling: minority students and students from poor families have markedly lower graduation rates—and take longer to earn degrees—even when other variables are taken into account. Noting the strong performance of transfer students and the effects of financial constraints on student retention, the authors call for improved transfer and financial aid policies, and suggest ways of improving the sorting processes that match students to institutions. An outstanding combination of evidence and analysis, Crossing the Finish Line should be read by everyone who cares about the nation's higher education system.

Basic Algebra and Advanced Algebra systematically develop concepts and tools in algebra that are vital to every mathematician, whether pure or applied, aspiring or established. Together, the two books give the reader a global view of algebra and its role in mathematics as a whole. The presentation includes blocks of problems that introduce additional topics and applications to science and engineering to guide further study. Many examples and hundreds of problems are included, along with a separate 90-page section giving hints or complete solutions for most of the problems.

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. 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.