This book is for K12 students who want to learn AI, for teachers who want to teach AI and bring AI into the classroom, and for any individual who wants to understand AI in a simple and effective way.
Artificial Intelligence is all around us. This book demystifies AI for K12 students and teachers using a unique combination of concept learning, hands-on plugged and unplugged exercises, context of how AI is used in industries from finance to marketing, and project ideas for students to apply their own creativity and build their own AIs. The ten fully illustrated color chapters cover both Machine Learning and Deep Learning, a comprehensive overview of AI Ethics, and popular AI algorithms from Linear Regression to Convolutional Neural Networks. Teacher’s corners provide teachers with additional resources for bringing AI into the classroom. The book is paired with extensive online resources in curriculum, datasets, exercises, and code.
The two authors (Nisha Talagala and Sindhu Ghanta) have extensive experience building industry AI solutions and have applied their knowledge to teach AI to K12 students. This book comes from their experiences of teaching AI to thousands of students around the world.
More information can be found on www.corp.aiclub.world/ai-book-middle-school-high-school
Tabella dei contenuti
Unit 1: Introduction to AI
Chapter 1 Fundamentals of AI
Chapter 2 How AIs Learn
Chapter 3 Data for AI
Chapter 4: AI Ethics 1
Unit 2: Introduction to Machine Learning
Chapter 5: Classification
Chapter 6: Regression
Unit 3: Introduction to Machine Learning Algorithms
Chapter 7: K Nearest Neighbor
Chapter 8: Linear Regression
Chapter 9: K-means clustering
Chapter 10: Deep Learning
Circa l’autore
Dr. Sindhu Ghanta is an expert in Artificial Intelligence and Machine Learning, with several publications and talks in world class journals and conferences. This book is an expression of her passion for teaching and making this amazing technology accessible to young minds. She is the co-founder of AIClub and works as the Head of Machine Learning. She received the Ph.D. degree in electrical and computer engineering from Northeastern University, Boston, USA, in 2014, followed by a Post-Doctoral Fellowship with BIDMC in the Department of Pathology, Harvard Medical School, where she was involved in detection and classification of features from histopathological (breast cancer) images. She worked as a research scientist with Parallel Machines on monitoring the health of machine learning algorithms in production and has many publications in the area.
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