A high-bias, low-variance introduction to Machine Learning for physicists

arXiv.org Machine Learning

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute. (Notebooks are available at https://physics.bu.edu/~pankajm/MLnotebooks.html )

Top Machine learning Books


Machine learning is to learn from data repetitively and to find the pattern hidden there. By applying the results of learning to new data, in other word Machine learning allows computers to analyze past data and predict future data. Machine learning is widely used in familiar places such as product recommendation system and face detection of photos. Also, as cloud machine learning services such as Microsoft's "Azure Machine Learning", Amazon's "Amazon Machine Learning", and Google's "Cloud Machine Learning" are released. This article is written to help novices and experts alike find the best Machine learning books to start with or continue their education. So here is a list of the best Machine learning Books: Book Name: Machine Learning This textbook provides a single source introduction to the primary approaches to machine learning Good content explained in very simple language. The book covers the concepts and techniques from the various fields in a unified fashion and very recent subjects such as genetic algorithms, re-enforcement learning and inductive logic programming. Writing style is clear, explanatory and precise.

11 Big Data Courses Will Help You Become Top Data Scientist JA Directives


Are you looking for Best Big Data Tutorial? Make your career standout with highly valuable skills by these Best Big Data Courses Online, Tutorial and Training. This courses will help you stay top in the industry on big data, data science, machine learning, deep learning, analytics and big data analytics. Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. These Big Data courses are hands-on to deliver the clues to the beginners and professionals about the major applications of Big Data Analytics fields.

From Insights to Interventions: Informed Design of Discussion Affordances for Natural Collaborative Exchange

AAAI Conferences

Despite studies showing collaboration to be beneficial both in terms of student satisfaction and learning, isolation is the norm in MOOCs. Two problems limiting the success of collaboration in MOOCs are the lack of support for team formation and structured collaboration support. Lack of support and strategies for team formation prevents teams from being set up for success from the beginning. Lack of structured support during synchronous collaboration has been demonstrated to produce significantly less learning than supported collaboration. This paper describes a deliberation based team formation approach and a scripted collaboration framework for MOOCs aimed at addressing these problems under the umbrella of Discussion Affordances for Natural Collaborative Exchange (DANCE) whose overarching focus is the enhancement of team-based MOOCs. These two examples of current work have been used as illustrations of insights informing interventions in MOOCs.

DynaLearn – An Intelligent Learning Environment for Learning Conceptual Knowledge

AI Magazine

Articulating thought in computer-based media is a powerful means for humans to develop their understanding of phenomena. We have created DynaLearn, an Intelligent Learning Environment that allows learners to acquire conceptual knowledge by constructing and simulating qualitative models of how systems behave. DynaLearn uses diagrammatic representations for learners to express their ideas. The environment is equipped with semantic technology components capable of generating knowledge-based feedback, and virtual characters enhancing the interaction with learners. Teachers have created course material, and successful evaluation studies have been performed. This article presents an overview of the DynaLearn system.