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  1. Lecture 1 of Leonard Susskind's Modern Physics concentrating on General Relativity. Recorded September 22, 2008 at Stanford University.

  2. This course is a first-semester freshman physics class in Newtonian Mechanics, Fluid Mechanics, and Kinetic Gas Theory. In addition to the basic concepts a variety of interesting topics are covered in this course: Binary Stars, Neutron Stars, Black Holes, Resonance Phenomena, Musical Instruments, Stellar Collapse, Supernovae, Astronomical observations from very high flying balloons (lecture 35), and you will be allowed a peek into the intr...more

  3. Most people are not very good at dealing in financial markets. Professional money managers, such as financial advisors and financial planners, assist individuals in matters of personal finance. FINRA and the SEC monitor the activities of these managers in order to protect individual investors. Mutual funds, exchange traded funds also exist to assist individual investments, and pension funds provide further services. These investment instit...more

  4. Lecture 20 continues the discussion of the value of life. It considers the neutral container theory, which holds that the value of life is simply a function of its contents, both pleasant and painful, and contrasts this with the valuable container theory, which assigns value to being alive itself. The lecture then turns to a consideration of some of the other aspects of death that may contribute to the badness of death. Among the issues ad...more

  5. In this lecture, Professor Paul Fry turns his attention to the relationship between authorship and the psyche. Freud's meditations on the fundamental drives governing human behavior are read through the lens of literary critic Peter Brooks. The origins of Freud's work on the "pleasure principle" and his subsequent revision of it are charted, and the immediate and constant influence of Freudian thought on literary production is asserted. Br...more

  6. The Motivation & Applications of Machine Learning, The Logistics of the Class, The Definition of Machine Learning, The Overview of Supervised Learning, The Overview of Learning Theory, The Overview of Unsupervised Learning, The Overview of Reinforcement Learning

  7. The lecture begins with further exploration of the question of whether it is desirable to live forever under the right circumstances, and then turns to consideration of some alternative theories of the nature of well-being. What makes a life worth living? One popular theory is hedonism, but the thought experiment of being on an "experience machine" suggests that this view may be inadequate.

  8. April 13, 2009 - Leonard Susskind reviews the Lagrange multiplier, explains Boltzmann distribution and Helm-Holtz free energy before oulining into the theory of fluctuations.

  9. In this lecture on queer theory, Professor Paul Fry explores the work of Judith Butler in relation to Michel Foucault's History of Sexuality. Differences in terminology and methods are discussed, including Butler's emphasis on performance and Foucault's reliance on formulations such as "power-knowledge" and "the deployment of alliance." Butler's fixation with ontology is explored with reference to Levi-Strauss's concept of the raw and the ...more

  10. Regulation of financial and securities markets is intended to protect investors while still enabling them to make personal investment decisions. Psychological phenomena, such as magical thinking, overconfidence, and representativeness heuristic can cause deviations from rational behavior and distort financial decision-making. However, regulation and regulatory bodies, such as the SEC, FDIC, and SIPC, most of which were created just after t...more

  11. Note: This course is offered by Stanford as an online course for credit. It can be taken individually, or as part of a master’s degree or graduate certificate earned online through the Stanford Center for Professional Development. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learnin...more