Operations In Base Ten Leveled Problem Addition Problem Solving Ring Toss
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Operations in Base Ten Leveled Problem: Addition Problem Solving--Ring Toss
Author: Linda Dacey, Ed.D.
language: en
Publisher: Teacher Created Materials
Release Date: 2014-01-01
Differentiate problem solving in your classroom using effective, research-based strategies. The problem-solving mini-lesson guides teachers in how to teach differentiated lessons. The student activity sheet features a problem tiered at three levels.
50 Leveled Math Problems Level 2
Author: Linda Dacey
language: en
Publisher: Teacher Created Materials
Release Date: 2012-04-01
Developed in conjunction with Lesley University, this engaging resource for second grade provides effective, research-based strategies to help teachers differentiate problem solving in the classroom. It includes: 50 leveled math problems (150 problems total), an overview of the problem-solving process, and ideas for formative assessment of students' problem-solving abilities. It also includes 50 mini-lessons and a student activity sheet featuring a problem tiered at three levels, plus digital resources that include electronic versions of activity sheets. This resource was developed with College and Career Readiness in mind, is aligned to the interdisciplinary themes from the Partnership for 21st Century Skills, and supports core concepts of STEM instruction.
Reinforcement Learning, second edition
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.