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The first text to integrate behavioral and cognitive approaches to learning and memory, this engaging textbook emphasizes human research, reflecting the field's evolution. Learning and Memory also recognizes the vital contribution of animal research, covering all historically important studies. Written in a lively and conversational style, this second edition encourages students to think critically. One example is its exploration of the Rescorla-Wagner model, the most important theory of conditioning, now further streamlined to improve student comprehension. Another is the addition of critical-thinking questions, which encourage students to evaluate their reactions to the material they've read, and relate findings to their own lives. Research includes an emphasis on practical applications such as treatments for phobias, addictions, and autism; the arguments for and against corporal punishment; whether recovered memories and eyewitness testimony should be believed; and effective techniques for studying. The text concludes with an overview of neural networks and deep learning.
The first text to integrate behavioral and cognitive approaches to learning and memory, this engaging textbook emphasizes human research, reflecting the field's evolution. Learning and Memory also recognizes the vital contribution of animal research, covering all historically important studies. Written in a lively and conversational style, this second edition encourages students to think critically. One example is its exploration of the Rescorla-Wagner model, the most important theory of conditioning, now further streamlined to improve student comprehension. Another is the addition of critical-thinking questions, which encourage students to evaluate their reactions to the material they've read, and relate findings to their own lives. Research includes an emphasis on practical applications such as treatments for phobias, addictions, and autism; the arguments for and against corporal punishment; whether recovered memories and eyewitness testimony should be believed; and effective techniques for studying. The text concludes with an overview of neural networks and deep learning.
Bridge the gap between theoretical concepts and their practical applications with this rigorous introduction to the mathematics underpinning data science. It covers essential topics in linear algebra, calculus and optimization, and probability and statistics, demonstrating their relevance in the context of data analysis. Key application topics include clustering, regression, classification, dimensionality reduction, network analysis, and neural networks. What sets this text apart is its focus on hands-on learning. Each chapter combines mathematical insights with practical examples, using Python to implement algorithms and solve problems. Self-assessment quizzes, warm-up exercises and theoretical problems foster both mathematical understanding and computational skills. Designed for advanced undergraduate students and beginning graduate students, this textbook serves as both an invitation to data science for mathematics majors and as a deeper excursion into mathematics for data science students.
Bridge the gap between theoretical concepts and their practical applications with this rigorous introduction to the mathematics underpinning data science. It covers essential topics in linear algebra, calculus and optimization, and probability and statistics, demonstrating their relevance in the context of data analysis. Key application topics include clustering, regression, classification, dimensionality reduction, network analysis, and neural networks. What sets this text apart is its focus on hands-on learning. Each chapter combines mathematical insights with practical examples, using Python to implement algorithms and solve problems. Self-assessment quizzes, warm-up exercises and theoretical problems foster both mathematical understanding and computational skills. Designed for advanced undergraduate students and beginning graduate students, this textbook serves as both an invitation to data science for mathematics majors and as a deeper excursion into mathematics for data science students.