Alfred Essa has led advanced analytics, machine learning, and information technology teams in academia and industry. He has served as Simon Fellow at Carnegie Mellon University, VP of Analytics and R&D at McGraw Hill Education, and CIO at MIT’s Sloan School of Management. He is a graduate of Haverford College and Yale University. Shirin Mojarad is a senior machine learning specialist at Google Cloud. Previously, she was a senior data scientist at Apple where she worked on AB experimentation, causal inference, and metrics design. She has experience applying AI and machine learning to five vertical markets in Big Data: healthcare, finance, educational technology, high tech, and cloud technology. She received her master’s and Ph.D. from Newcastle University, United Kingdom.

• Introduction What is AI and why it is at the center of major business transformation? How is it related to machine learning? What is deep learning, and how is it related to ML? Why is it important? How the book is organized Who is the audience? Section 1: Machine Learning　Chapter 1.1, introduction, machine learning, different types of machine learning　 Chapter 1.2,　Machine Learning Technical Overview　 Chapter 1.3, Hands-On Machine Learning with Scikit Learn Chapter 1.4,　　Advanced Topics/flavors of Machine learning Appendix: mathematical interlude Section 2: Deep Learning　 Chapter 2.1, introduction (what is it, why is it important) Chapter 2.2,　Deep Learning Technical Overview　 Chapter 2.3, Hands-On Deep Learning with Keras Chapter 2.4,　　Advanced Topics/flavors of deep learning Appendix: mathematical interlude Section 3: Putting AI into Practice: Innovation Framework Chapter 3.1: Diffusion and Dynamics of Innovation Chapter 3.2: Managing an Innovation Portfolio

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Alfred Essa has led advanced analytics, machine learning, and information technology teams in academia and industry. He has served as Simon Fellow at Carnegie Mellon University, VP of Analytics and R&D at McGraw Hill Education, and CIO at MIT’s Sloan School of Management. He is a graduate of Haverford College and Yale University. Shirin Mojarad is a senior machine learning specialist at Google Cloud. Previously, she was a senior data scientist at Apple where she worked on AB experimentation, causal inference, and metrics design. She has experience applying AI and machine learning to five vertical markets in Big Data: healthcare, finance, educational technology, high tech, and cloud technology. She received her master’s and Ph.D. from Newcastle University, United Kingdom.

• Introduction What is AI and why it is at the center of major business transformation? How is it related to machine learning? What is deep learning, and how is it related to ML? Why is it important? How the book is organized Who is the audience? Section 1: Machine Learning　Chapter 1.1, introduction, machine learning, different types of machine learning　 Chapter 1.2,　Machine Learning Technical Overview　 Chapter 1.3, Hands-On Machine Learning with Scikit Learn Chapter 1.4,　　Advanced Topics/flavors of Machine learning Appendix: mathematical interlude Section 2: Deep Learning　 Chapter 2.1, introduction (what is it, why is it important) Chapter 2.2,　Deep Learning Technical Overview　 Chapter 2.3, Hands-On Deep Learning with Keras Chapter 2.4,　　Advanced Topics/flavors of deep learning Appendix: mathematical interlude Section 3: Putting AI into Practice: Innovation Framework Chapter 3.1: Diffusion and Dynamics of Innovation Chapter 3.2: Managing an Innovation Portfolio

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Alfred Essa has led advanced analytics, machine learning, and information technology teams in academia and industry. He has served as Simon Fellow at Carnegie Mellon University, VP of Analytics and R&D at McGraw Hill Education, and CIO at MIT’s Sloan School of Management. He is a graduate of Haverford College and Yale University. Shirin Mojarad is a senior machine learning specialist at Google Cloud. Previously, she was a senior data scientist at Apple where she worked on AB experimentation, causal inference, and metrics design. She has experience applying AI and machine learning to five vertical markets in Big Data: healthcare, finance, educational technology, high tech, and cloud technology. She received her master’s and Ph.D. from Newcastle University, United Kingdom.

• Introduction What is AI and why it is at the center of major business transformation? How is it related to machine learning? What is deep learning, and how is it related to ML? Why is it important? How the book is organized Who is the audience? Section 1: Machine Learning　Chapter 1.1, introduction, machine learning, different types of machine learning　 Chapter 1.2,　Machine Learning Technical Overview　 Chapter 1.3, Hands-On Machine Learning with Scikit Learn Chapter 1.4,　　Advanced Topics/flavors of Machine learning Appendix: mathematical interlude Section 2: Deep Learning　 Chapter 2.1, introduction (what is it, why is it important) Chapter 2.2,　Deep Learning Technical Overview　 Chapter 2.3, Hands-On Deep Learning with Keras Chapter 2.4,　　Advanced Topics/flavors of deep learning Appendix: mathematical interlude Section 3: Putting AI into Practice: Innovation Framework Chapter 3.1: Diffusion and Dynamics of Innovation Chapter 3.2: Managing an Innovation Portfolio

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