Machine Learning

Immink
Kees Schouhamer Immink
President and founder of Turing Machines Inc., a Dutch-based research and consulting firm that contributes to science and technology., Ph.D. - Eindhoven University of Technology.


Deep
Learning


Bengio
Yoshua Bengio
Professor of the Department of Computer Science and Operations Research, head of the Montreal Institute for Learning Algorithms (MILA), CIFAR Program co-director of the CIFAR program on Learning in Machines and Brains, and Canada Research Chair in Statistical Learning Algorithms., Ph.D. - McGill University.


Pattern Recognition
and Machine Learning

Deep
Learning


Zisserman
Andrew Zisserman
Professor of Computer Vision Engineering in the Department of Engineering Science, University of Oxford, Ph.D. - Sunderland Polytechnic.


Pattern Recognition
and Machine Learning

The Elements of
Statistical Learning


Giannakis
Georgios Giannakis
Endowed Chair in Wireless Telecommunications, and McKnight Presidential Chair in ECE Digital Technology Center, Director University of Minnesota, Minneapolis. Ph.D. - University of Southern California


The Elements of
Statistical Learning


Koller
Daphne Koller
Previous Professor of Computer Science at Stanford University and a MacArthur Fellowship recipient. She is one of the founders of Coursera, an online education platform, and is founder and CEO of Insitro, a drug discovery startup. Ph.D. - Stanford University.


Deep
Learning

Deep Learning for
the Life Sciences

Pattern Recognition
and Machine Learning


Schapire
Robert Schapire
Principal Researcher at Microsoft Research in New York City, and a Visiting Lecturer in Computer Science at Princeton University. Ph.D. - MIT


Prediction, Learning,
and Games

Understanding
Machine Learning

Reinforcement
Learning

Probably Approximately
Correct


Tomasi
Carlo Tomasi
The Iris Einheuser Professor of Computer Science, Duke University. Ph.D. - Carnegie Mellon University.


Understanding
Machine Learning

Information Theory,
Inference and Learning

The Elements of
Statistical Learning

Foundations of
Machine Learning


Tarokh
Vahid Tarokh
The Rhodes Family Professor of Electrical and Computer Engineering, Duke University. Ph.D. - University of Waterloo.


Pattern Recognition
and Machine Learning


Crowcroft
Bradley Efron
Max H. Stein Professor and Professor of Statistics and of Biomedical Data Ssience at Stanford University. Ph.D. - Stanford.


The Elements of
Statistical Learning


LeCun
Yann LeCun
Silver Professor of the Courant Institute of Mathematical Sciences at New York University, and Vice President, Chief AI Scientist at Facebook. Ph.D. - Université Pierre et Marie Curie, Paris.

Note: the following books are not recommended by Professor LeCun. They are books that have been used as reference texts in one/some courses he has taught.


Pattern Recognition
and Machine Learning

Pattern
Classification

Introduction to
Machine Learning

The Elements of
Statistical Learning

Neural Networks for
Pattern Recognition

Neural Networks: A
Comprehensive …

Machine
Learning


Manning
Christopher Manning
Thomas M. Siebel Professor in Machine learning, Professor of linguistics and computer science, Director, Stanford Artificial Intelligence Laboratory (SAIL), Associate Director, Human-Centered Artificial Intelligence Institute, Stanford University. Ph.D. Stanford.

Note: the following books are not recommended by Professor Manning. They are books that have been used as reference texts in one/some courses he has taught.


Introduction to
Deep Learning

Neural Networks and
Deep Learning


Machine learning is a field of computer science that gives computer systems the ability to “learn” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.

The name Machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank, and computer vision.

Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.

Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to “produce reliable, repeatable decisions and results” and uncover “hidden insights” through learning from historical relationships and trends in the data.


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