Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.

The Master Algorithm: How the Quest for the Ultimate Learning Machi... by Pedro Domingos Superforecasting: The Art and Science of Prediction by Philip E. Tetloc Fundamentals of Machine Learning for Predictive Data Analytics: Alg... by John D. Kelleher Machine Learning: The Art and Science of Algorithms that Make Sense... by Peter Flach Machine Learning: A Bayesian and Optimization Perspective by Sergios Theodoridis Machine Learning for Evolution Strategies by Oliver Kramer Essential Algorithms: A Practical Approach to Computer Algorithms by Rod Stephens How Not to Be Wrong: The Power of Mathematical Thinking by Jordan Ellenberg Assessing and Improving Prediction and Classification by Timothy Masters All of Statistics: A Concise Course in Statistical Inference by Larry Wasserman The Elements of Statistical Learning: Data Mining, Inference, and P... by Trevor Hastie Causal Inference in Statistics: A Primer by Judea Pearl