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Louis Wonsley
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Yaser Abu-mostafa Learning From



This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures below follow each other in a story-like fashion:




Yaser Abu-mostafa Learning From



Yaser S. Abu-Mostafa is a Professor of Electrical Engineering and Computer Science at the California Institute of Technology, Chairman of Paraconic Technologies Ltd, and Chairman of Machine Learning Consultants LLC. His main fields of expertise are Machine Learning, Artificial Intelligence, and Computational Finance. He is the author of Amazon's machine learning bestseller Learning from Data. His MOOC on machine learning has attracted more than seven million views.


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Welcome Message from the AuthorsMachine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover.


Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems.


Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own.


This course covers the theory, algorithms, and applications of machine learning (a.k.a. computational learning or statistical learning, with significant overlap with data mining and pattern recognition). It is a subject that combines mathematical theory with heuristic techniques, and it is one of the most widely applicable subjects in engineering and scientific research as well as in practical applications from computational finance to recommender systems to medical applications to robotics, among other fields.


We introduce a novel application of support vector machines (SVM), an important machine learning algorithm, to determine the beginning and end of recessions in real time. Nowcasting, forecasting a condition in the present time because the full information will not be available until later, is key for recessions, which are only determined months after the fact. We show that SVM has excellent predictive performance for this task, capturing all six recessions from 1973 to 2018 and providing the signal with minimal delay. We take advantage of the timeliness of SVM signals to test dynamic asset allocation between stocks and bonds. A dynamic risk budgeting approach using SVM outputs appears superior to an equal-risk contribution portfolio, improving the average returns by 85 bps per annum without increased tail risk.


This paper provides a brief introduction to forecasting in financial markets with emphasis on commodity futures and foreign exchange. We describe the basic approaches to forecasting, and discuss the noisy nature of financial data. Using neural networks as a learning paradigm, we describe different techniques for choosing the inputs, outputs, and error function. We also describe the learning from hints technique that augments the standard learning from examples method. We demonstrate the use of hints in foreign-exchange trading of the U.S. Dollar versus the British Pound, the German Mark, the Japanese Yen, and the Swiss Franc, over a period of 32 months. The paper does not assume a background in financial markets.


The basic paradigm for learning in neural networks is 'learning from examples' where a training set of input-output examples is used to teach the network the target function. Learning from hints is a gen(cid:173) eralization of learning from examples where additional information about the target function can be incorporated in the same learning process. Such information can come from common sense rules or special expertise. In financial market applications where the train(cid:173) ing data is very noisy, the use of such hints can have a decisive advantage. We demonstrate the use of hints in foreign-exchange trading of the U.S. Dollar versus the British Pound, the German Mark, the Japanese Yen, and the Swiss Franc, over a period of 32 months. We explain the general method of learning from hints and how it can be applied to other markets. The learning model for this method is not restricted to neural networks.


Machine Learning is concerned with computer programs that automatically improve their performance through experience. This 3-credit course covers introductory topics about the theory and practical algorithms for machine learning from a variety of perspectives. Topics include supervised learning, unsupervised learning and learning theory.


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