Engage your mathematics students at the beginning of class with this whole-class warm-up activity. This product features a step-by-step lesson, assessment information, and a snapshot of what the warm-up looks like in the classroom.

Daily Math Stretches offers practice in algebraic thinking, geometry, measurement, and data for grades 3-5 to provide an early foundation for mastering mathematical learning. Written by Guided Math author Laney Sammons and with well-known, research-based approaches, this product provides step-by-step lessons, assessment information, and a snapshot of how to facilitate these math discussions in your classroom. Digital resources are also included for teacher guidance with management tips, classroom set-up tips, and interactive whiteboard files for each stretch.

Engage your mathematics students at the beginning of class with this whole-class warm-up activity. This product features a step-by-step lesson, assessment information, and a snapshot of what the warm-up looks like in the classroom.

Jumpstart your students' minds with daily warm-ups that get them thinking mathematically and ready for instruction. Daily Math Stretches offers practice in algebraic thinking, geometry, measurement, and data for grades 3-5 to provide an early foundation for mastering mathematical learning. Written by Guided Math author Laney Sammons and with well-known, research-based approaches, this product provides step-by-step lessons, assessment information, and a snapshot of how to facilitate these math discussions in your classroom. Digital resources are also included for teacher guidance with management tips, classroom set-up tips, and interactive whiteboard files for each stretch.

Making Mathematical Arguments, Student Guide

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.

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.