A First Course in Machine Learning
Simon Rogers
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Understanding Machine Learning : From Theory to Algorithms
Shai (Hebrew University of Jerusalem) Shalev-Shwartz
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Fundamentals of Machine Learning for Predictive Data Analytics
John D. Kelleher
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Introduction to Machine Learning
Ethem (Ozyegin University) Alpaydin
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Introduction to Natural Language Processing
Jacob (Assistant Professor, Google) Eisenstein
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Reasoning about Uncertainty
Joseph Y. (Cornell University) Halpern
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Probabilistic Reasoning in Intelligent Systems : Networks of Plausible Inference
Judea Pearl
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Foundations of Machine Learning
Mehryar (New York University) Mohri
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High-Dimensional Statistics : A Non-Asymptotic Viewpoint
Martin J. (University of California, Berkeley) Wainwright
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Decision Making Under Uncertainty : Theory and Application
Mykel J. (Stanford University) Kochenderfer
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Data-Driven Science and Engineering : Machine Learning, Dynamical Systems, and Control
Steven L. (University of Washington) Brunton
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Introduction to AI Robotics
Robin R. (Raytheon Professor of Computer Science & Engineering Murphy
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Introduction to Machine Learning with Applications in Information Security
Mark Stamp
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The Principles of Deep Learning Theory : An Effective Theory Approach to Understanding Neural Networks
Daniel A. (Massachusetts Institute of Technology) Roberts
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Machine Learning Refined : Foundations, Algorithms, and Applications
Jeremy (Northwestern University, Illinois) Watt
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Machine Learning : A Bayesian and Optimization Perspective
Sergios (professor of machine learning and signal process Theodoridis
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Natural Language Processing with Transformers, Revised Edition
Lewis Tunstall
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Machine Learning for Factor Investing: R Version
Guillaume Coqueret
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Data Mining and Machine Learning : Fundamental Concepts and Algorithms
Mohammed J. (Rensselaer Polytechnic Institute, New York) Zaki
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Mining of Massive Datasets
Jure (Stanford University, California) Leskovec
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High-Dimensional Data Analysis with Low-Dimensional Models : Principles, Computation, and Applications
John (Columbia University, New York) Wright
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Machine Learning : A First Course for Engineers and Scientists
Andreas Lindholm
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Mathematical Aspects of Deep Learning
Philipp (Universitat Wien, Austria) Grohs
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