lecture

Harvard physicist Eric Mazur explains how he realized his students were memorizing rather than understanding, and how he changed his teaching. He presents peer instruction and data showing improved student performance.

The first lecture of Leonard Susskind's Stanford Continuing Studies course on modern physics, beginning with classical mechanics. It is the start of a six-quarter sequence on the theoretical foundations of physics.

The introductory lecture of Stephen Boyd's Stanford Convex Optimization I course (EE 364A). It outlines convex sets, functions and problems, and the course's coverage of least-squares, linear and semidefinite programming, duality and interior-point methods.

First lecture of Stanford's CS106A Programming Methodology by Mehran Sahami, giving a course overview and starting on programming. The course teaches software engineering principles using Java.

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.

Randy Pausch's 2007 last lecture at Carnegie Mellon, given as he was dying of pancreatic cancer. He reflects on achieving childhood dreams, overcoming obstacles and helping others reach theirs.

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.

Carnegie Mellon professor Randy Pausch gives a lecture on time management at the University of Virginia in 2007. He covers practical habits such as tackling important tasks first and distinguishing important from urgent work.

The first lecture of Stanford's CS107 Programming Paradigms, taught by Jerry Cain, giving an overview of the course. The course goes from C and assembly through C++, concurrency, Scheme and Python.

Lecture 1B of MIT's 6.001 Structure and Interpretation of Computer Programs (1986) with Gerald Sussman, covering procedures, processes and the substitution model. It includes the line about the power of naming things.

Lecture 7A of the 1986 MIT 6.001 SICP course, with Gerald Sussman presenting the metacircular evaluator, part 1. It shows how a Lisp evaluator can be written in Lisp itself.

The first lecture of Andrew Ng's Stanford CS229 Machine Learning course, giving an overview of the course. It introduces supervised and unsupervised learning, learning theory and reinforcement learning.

The first episode of Michael Sandel's Harvard course on moral reasoning, opening with the trolley problem and the choice between killing one to save five. Sandel draws out student arguments and introduces utilitarian and other approaches to justice.

The first lecture of Gilbert Strang's MIT 18.06 Linear Algebra course, covering the geometry of linear equations. It opens a full 35-lecture university course.

Part 1 of a lecture by physicist Albert Bartlett on the exponential function and its consequences for population growth, resource use and energy. It argues that steady growth rates quickly lead to enormous quantities.

Elizabeth Warren's 2007 Berkeley lecture, when she was a Harvard law professor, on why middle-class families are under growing financial strain. She draws on bankruptcy research to examine rising costs of housing, health care and education.

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

Ian Piumarta's 2007 Stanford EE380 lecture on building a dynamic programming environment from the ground up, from the Viewpoints Research Institute's work. It covers the design of a language and system with a recursive design, parsing through code generation, and the COLA architecture.

First lecture of MIT 6.00, an introductory computer science course taught in Python by Eric Grimson and John Guttag. It covers the goals of the course, what computation is, and data types, operators and variables.

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 1981 interview clip in which Richard Feynman answers an artist friend who claims science takes the beauty out of a flower. Feynman argues that understanding adds to beauty and mystery.

Steve Blank's Computer History Museum lecture argues that Silicon Valley's roots lie in WWII radar and electronic warfare work, Stanford's Fred Terman and Cold War military funding rather than the semiconductor alone.

A one-minute clip from Richard Feynman's 1979 Auckland lecture in which he answers whether you see an object or only its light. It is a short, playful take on perception and on the use of philosophy.

Chalmers Johnson, author of Blowback and Sorrows of Empire, lectures on parallels between the decline of the Roman and Soviet empires and the United States. He argues the US shows the same symptoms: overextension, corruption, and an inability to reform.

Steve Blank tells how World War II, Stanford's Frederick Terman and military and intelligence agencies shaped the rise of Silicon Valley. The talk centers on the Cold War electronic warfare and signals intelligence work that seeded the region's tech industry.

Ted Nelson demonstrates Xanadu Space, his software for representing hypertext in three-dimensional space with linked, referenced content fragments.

A PBS clip in which Richard Feynman explains the strange nature of light and electromagnetic radiation. He conveys how little of it we actually see and how radio waves are present everywhere.

A clip from the 1983 BBC series Fun to Imagine in which Richard Feynman explains heat as the jiggling of atoms and how we perceive it as hot and cold.

A short clip from Carl Sagan's Cosmos in which he explains how Eratosthenes measured the circumference of the Earth using shadows in two Egyptian cities. It is a clear, visual account of an ancient scientific feat.

Psychologist Barry Schwartz argues in this TED talk that abundant choice makes people more paralyzed and dissatisfied rather than freer and happier.

Mike Rowe, host of Dirty Jobs, shares stories from messy real-world jobs in this TED talk. He argues that hard work has been unjustly degraded in society.

Earliest known footage of Ivan Sutherland's MIT PhD work, Sketchpad, the first computer graphics system, with commentary by Alan Kay. It shows light-pen drawing with constraints in a short clip.

A short clip of Richard Feynman answering an interviewer's question about how magnets work, in which he explains why a why-question has no simple answer without a shared frame of reference.

A segment from Carl Sagan's original Cosmos explaining the fourth spatial dimension through analogy with a two-dimensional world and shadow projection.

An eight-minute excerpt of a 1983 video featuring Seymour Papert, creator of Logo, on children learning programming and abstract ideas through embodied play. It shows kids acting out the Logo turtle.

Kirk McKusick gives a narrative history of BSD from its start at UC Berkeley in the late 1970s through the 1980s releases and the lawsuit that led to 4.4BSD-Lite in 1992. It is full of anecdotes from a key developer.

Sir Ken Robinson argues, with humor and stories such as that of choreographer Gillian Lynne, that education systems undermine creativity and should be changed. It is one of the most viewed TED talks.

Karl Fogel's 2006 Google Tech Talk traces copyright from a 16th-century English censorship law to a distribution monopoly. It then surveys alternative economic bases for creation and distribution.

Richard Feynman, in a clip from the BBC series Fun to Imagine, takes on the puzzle of why mirrors seem to swap left and right but not top and bottom.

Peter Norvig's 2007 Google talk on theorizing from data, covering what huge datasets can do for language understanding, translation, information extraction and inference. It argues that more data often beats cleverer models.