data science

Peter Norvig's UBC distinguished lecture on how massive amounts of simple data, such as for language modeling, can yield understanding that hand-built models could not.

Database pioneer Michael Stonebraker argues that 'big data' is really at least four different problems: big volumes with small analytics, big analytics, velocity, and variety. He comments on where each market is heading.

The first lecture of Andy Pavlo's CMU 15-445 Intro to Database Systems (Fall 2019), covering course introduction and the relational model.

A Berkeley Lab Distinguished Lecturer talk on building science, with an expert covering control layers, insulation and vapour barrier placement, and how buildings fail. The delivery is humorous.

An excerpt from Hal Abelson's opening SICP lecture introducing computer science and contrasting declarative and imperative knowledge. He argues that computer science is not really a science and is more like magic or engineering of ideas.

A two-minute clip of Richard Feynman on the social sciences, discussing what counts as real science versus pseudo science.

A guest lecture Jon Gjengset gave at Two Sigma on using Rust for large, high-performance systems. It covers why Rust suits such projects, the Noria research database, a concurrent data structure used in it, and practical experience.

Matt Parker's LMS popular lecture on how seemingly incredible results can be meaningless random patterns. He ranges from ancient Woolworths civilisations to shoelaces and DNA.

Hans Rosling uses data and animated statistics to show how world population is changing, why birth rates fall as child survival and wealth rise, and why population is expected to peak around 11 billion. It also tackles common misconceptions about Africa and family planning.

Neuroscientist Anil Seth gives a Royal Institution lecture on the science of consciousness. He separates conscious level, content and self, and argues that experience arises from the brain's predictive inference.

The introductory lecture of Richard Hamming's capstone course, The Art of Doing Science and Engineering: Learning to Learn. It focuses on style of thinking and on educating rather than training, using technical material only as illustration.

Erik Demaine's MIT 6.851 Advanced Data Structures lecture introducing succinct data structures, covering rank, select and tries. It is part of the Spring 2012 course.

The first lecture of MIT 6.042J Mathematics for Computer Science, taught by Tom Leighton, introducing mathematical proofs. It covers propositions, implication and basic proof methods.

Jaron Lanier delivers the 2018 Willy Brandt Lecture in Berlin on who civilization is for, covering data, AI, tech platforms and the economics of who benefits, followed by a Q&A with Germany's data protection commissioner Ulrich Kelber.

Floyd Toole's CIRMMT distinguished lecture on the science of sound reproduction, drawing on decades of blind listening research. It covers what listeners prefer in loudspeakers and why measured accuracy and anechoic response matter.

Chris Granger, creator of Light Table and the Eve language, gives a Berkeley data science lecture on what computation and programming might look like in ten years. He covers the path that led to Eve and ends with demos.

Turing Award winner Michael Stonebraker gives the 2018 Donald B. Gillies Memorial Lecture at Illinois. He discusses the gap between academic research and industry problems, publishing incentives, and the influence of large industry users on research.

The first lecture of MIT's 1986 6.001 Structure and Interpretation of Computer Programs, given by Harold Abelson, introducing Lisp and the idea of computer science as the study of process.

A lecture from MIT's Missing Semester course covering version control with git. It builds up git from its data model rather than from commands.

A lecture by Richard Feynman on how computers work, using the analogy of a filing clerk that becomes progressively dumber and faster. It also covers heuristics and how computers handle data and solve problems in finite time.

Michael Betancourt's lecture at MLSS Iceland 2014 on efficient Bayesian inference with Hamiltonian Monte Carlo. It covers MCMC concepts and motivation and the problems of sampling in high-dimensional distributions.

Part one of Capt. Grace Hopper's 1982 NSA lecture on the future of data, hardware, software and people. It includes her humor, her nanosecond demonstration and views on scaling and resisting old ways of doing things.

Neuroscientist Karl Friston gives a 15-minute talk on the free energy principle, covering Markov blankets, Bayesian model evidence and global brain theories.

A short excerpt from Dr Russell Barkley's 2012 Burnett Lecture on ADHD, focused on treatment. He argues ADHD is a disorder of executive function and self-regulation, and that environments should be designed to compensate.

A short clip of Richard Feynman explaining the scientific method: guess, compute consequences, compare to experiment, and why vague theories like astrology cannot be proven wrong.

The first episode of John Vervaeke's lecture series on the modern meaning crisis, tracing its history through philosophy, cognitive science, religion and psychology. It introduces the series and its themes.

Doug McGuff presents the biochemistry behind high-intensity strength training and the paleo diet, including how the body stores and burns fat. It is a roughly hour-and-a-half full-length lecture.

Bill Hammack of engineerguy introduces his series on Michael Faraday's lectures on The Chemical History of a Candle. He describes the free companion book and the commentary and caption features that help modern viewers.

Tom Wildenhain's guest lecture for CMU's Esoteric Programming Languages course shows that PowerPoint transitions and slides are Turing complete without macros, building up to a mind-bending fractal ending.

Andres Gomez Emilsson of the Qualia Research Institute presents a model of DMT experiences using hyperbolic geometry and dynamical systems theory. He describes how different intoxication levels correspond to changing curvature and information content of experience.

A one-minute clip of Richard Feynman describing the scientific method: guess a law, compute its consequences, and compare with experiment. If it disagrees with experiment, it is wrong.

Brian Kernighan gives a talk at the University of Nottingham on how to succeed in language design without really trying. He draws on his experience with awk and other languages.

A three-minute clip of Richard Feynman using the rules of chess as an analogy for how science works, discovering rules by observation and sometimes meeting surprises like castling. It touches on incremental progress versus revolutions.

The opening lecture of Robert Sapolsky's Stanford course on Human Behavioral Biology, explaining the course premise and how to avoid categorical thinking. It starts a roughly 25-lecture series on how biology, genes, hormones and environment shape behavior.

Stanford professor Robert Sapolsky lectures on depression as a real biological disease. He covers its biological and psychological causes, how it differs from ordinary sadness, and ideas such as learned helplessness. He ties these together into one account.

Douglas Hofstadter's Stanford Presidential Lecture argues that analogy is the core of cognition, illustrating how concepts, categorization and thought arise from analogy-making.

The first of Richard Feynman's 1964 Cornell Messenger Lectures, The Character of Physical Law, focusing on the law of gravitation. Feynman builds up from planetary motion to the nature of physical laws.

Sean Carroll introduces his video series on the biggest ideas in physics, such as symmetry, space and matter, as informal discussions with follow-up Q&A videos. This is the short opening video of the series.

Robert Sapolsky's lecture on how the body's stress response, evolved for short-term physical emergencies, harms long-term health when triggered by psychological stressors. It was given as the inaugural Fenton-Rhodes Lecture.

The opening lecture of Steve Brunton's Control Bootcamp, covering the kinds of control and why closed-loop feedback matters. It starts a series that follows the control chapter of the Brunton and Kutz data-driven science book.