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Maximizing the learning environment and focusing on the principles of learning are the most critical needs facing educational leaders of every rank. The Learning Equation: The Education Process and Effective Schools, Teachers, and Students develops a “learning equation” that depicts various learning situations based upon the effectiveness of the school environment and the degree to which students want to learn. The book begins with a practical framework that provides educational leaders with a means of creating an environment that will maximize student learning. The second part of the book underscores important aspects of learning that will help both students and educational leaders. The information in the two parts of the book is captured by the development of ‘the learning equation’ which predicts student academic performance. The learning equation cuts through political and educational ideologies and focuses on reality. Ultimately, the learning equation highlights how student achievement can be finally improved.
Consists of 65 interactive lessons on college prealgebra. Topics covered: whole numbers, fractions and decimals, integers, rational numbers and sqare roots, the language of algebra, linear equations, polynomials, ratio and percent, measurement, area, perimeter and volume, polygons and circles.
Copublished with the National Council of Teachers of Mathematics, this book focuses on individuals involved in K 12 mathematics education particularly educators and parents who seek to improve their understanding of mathematics and help students succeed. The authors tackle popular misconceptions and misguided discourse about mathematics education and draw on peer-reviewed research about instruction that can significantly improve student learning."
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Make formative assessment work for you—and your math students! Finally, formative assessment that adds up! Bringing Math Students Into the Formative Assessment Equation is the ultimate resource for helping teachers implement formative assessment in the middle school mathematics classroom. And it’s much more than that. With this research-based, teacher-tested guide, you won’t just learn effective teaching strategies—you’ll turn your students into self-regulated learners. They’ll monitor and assess their own progress—and communicate to you about it! Features include: A clear and manageable six-aspect instructional model Detailed strategies for helping students own their successes Real-life examples from middle school mathematics teachers Useful resources and a companion website to help you implement formative assessment in your classroom Formative assessment isn’t just for teachers anymore. With the help of this essential resource, you’ll work together with your students toward a common goal of math success. "This book is outstanding. I would recommend it to any math educator. The depth of research integrated into practice is extensive and, as a result, it is the most practical book I have come across related to formative assessment and mathematics The self-regulation aspects, as well as the ownership and involvement emphasized in the book, went beyond the traditional cognitive strategies endorsed in most books." Marc Simmons, Principal Ilwaco Middle School, Ocean Beach School District, Long Beach, WA "The ideas in this book are brought to life with examples of teachers and students in the classroom. The teacher voices, comments, and quotes lend credibility and are a big component of the book’s strengths as well as the visuals and graphics." Rita Tellez, Math Coordinator Ysleta Independent School District, El Paso, TX
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions
Includes music.