Four lectures on probabilistic methods for data science

Methods of high-dimensional probability play a central role in applications for statistics, signal processing theoretical computer science and related fields. These lectures present a sample of particularly useful tools of high-dimensional probability, focusing on the classical and matrix Bernstein's inequality and the uniform matrix deviation inequality. We illustrate these tools with applications for dimension reduction, network analysis, covariance estimation, matrix completion and sparse signal recovery. The lectures are geared towards beginning graduate students who have taken a rigorous course in probability but may not have any experience in data science applications.Comment: Lectures given at 2016 PCMI Graduate Summer School in Mathematics of Data. Some typos, inaccuracies fixe

The emergence of French statistics. How mathematics entered the world of statistics in France during the 1920s

This paper concerns the emergence of modern mathematical statistics in France after the First World War. Emile Borel's achievements are presented, and especially his creation of two institutions where mathematical statistics was developed: the {\it Statistical Institute of Paris University}, (ISUP) in 1922 and above all the {\it Henri Poincar\'e Institute} (IHP) in 1928. At the IHP, a new journal {\it Annales de l'Institut Henri Poincar\'e} was created in 1931. We discuss the first papers in that journal dealing with mathematical statistics

Measurement with Persons: A European Network

The European ‘Measuring the Impossible’ Network MINET promotes new research activities in measurement dependent on human perception and/or interpretation. This includes the perceived attributes of products and services, such as quality or desirability, and societal parameters such as security and well-being. Work has aimed at consensus about four ‘generic’ metrological issues: (1) Measurement Concepts & Terminology; (2) Measurement Techniques: (3) Measurement Uncertainty; and (4) Decision-making & Impact Assessment, and how these can be applied specificallyto the ‘Measurement of Persons’ in terms of ‘Man as a Measurement Instrument’ and ‘Measuring Man.’ Some of the main achievements of MINET include a research repository with glossary; training course; book; series of workshops;think tanks and study visits, which have brought together a unique constellation of researchers from physics, metrology,physiology, psychophysics, psychology and sociology. Metrology (quality-assured measurement) in this area is relativelyunderdeveloped, despite great potential for innovation, and extends beyond traditional physiological metrology in thatit also deals with measurement with all human senses as well as mental and behavioral processes. This is particularlyrelevant in applications where humans are an important component of critical systems, where for instance health andsafety are at stake

Teaching Quantum Interpretations: Revisiting the goals and practices of introductory quantum physics courses

Most introductory quantum physics instructors would agree that transitioning students from classical to quantum thinking is an important learning goal, but may disagree on whether or how this can be accomplished. Although (and perhaps because) physicists have long debated the physical interpretation of quantum theory, many instructors choose to avoid emphasizing interpretive themes; or they discuss the views of scientists in their classrooms, but do not adequately attend to student interpretations. In this synthesis and extension of prior work, we demonstrate: (1) instructors vary in their approaches to teaching interpretive themes; (2) different instructional approaches have differential impacts on student thinking; and (3) when student interpretations go unattended, they often develop their own (sometimes scientifically undesirable) views. We introduce here a new modern physics curriculum that explicitly attends to student interpretations, and provide evidence-based arguments that doing so helps them to develop more consistent interpretations of quantum phenomena, more sophisticated views of uncertainty, and greater interest in quantum physics.Comment: 14 pages, 11 figures; submitted to PRST-PER: Focused Collection on Upper-Division PER. arXiv admin note: text overlap with arXiv:1409.849

Learning sentiment from students’ feedback for real-time interventions in classrooms

Knowledge about users sentiments can be used for a variety of adaptation purposes. In the case of teaching, knowledge about students sentiments can be used to address problems like confusion and boredom which affect students engagement. For this purpose, we looked at several methods that could be used for learning sentiment from students feedback. Thus, Naive Bayes, Complement Naive Bayes (CNB), Maximum Entropy and Support Vector Machine (SVM) were trained using real students' feedback. Two classifiers stand out as better at learning sentiment, with SVM resulting in the highest accuracy at 94%, followed by CNB at 84%. We also experimented with the use of the neutral class and the results indicated that, generally, classifiers perform better when the neutral class is excluded