과 정 명 : 
: 웹표준&드림위버 
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: 
: 15~20 명 (상담면접 후 접수) 
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: 능력개발카드제 수강지원금 사업주지원 
: 02-3481-1005 - Machine Learning at Stanford by Andrew Ng
- Game Theory (elementary) by Benjamin Polak at Yale
- Online Learning, Regret Minimization, and Game Theory by Avrim Blum
- contextual bandit Learning through Exploration by Alina Beygelzimer and John Langford
- Statistical Learning Theory by John Shawe-Taylor: it's rather slow, the one-before-last video is missing and the last video has slides from the missing video instead so I gave up on watching the last vid; but it's a hands-on focused introduction to the PAC theory
- Foundations of Machine Learning by Marcus Hutter is a more fleshed-out variant of Universal Artificial Intelligence, the latter also shows recent approximations / experimental results (both are not going into details), see also Ray Solomonoff (read by Marcus Hutter) - Algorithmic Probability, Heuristic Programming and AGI
- Heuristics, Probability and Causality: Judea Pearl Tribute Symposium
- (Model-based Reinforcement Learning by Michael Littmanor Reinforcement learning by Scott Sanner), then Richard Sutton - AGI 10 Keynote Address (part 2), then Hamid Reza Maei - GQ(lambda)- A General Gradient Algorithm for Temporal-Difference Prediction Learning with Eligibility Traces
- Deep Belief Networks by Geoffrey E. Hinton (or perhaps it was the old Google TechTalk version of this lecture); this TechTalk is a bit more recent; Unsupervised Feature Learning and Deep Learning by Andrew Ng (with sparse coding)
- Lecture series on advanced (functional) programming concepts by Ralf Lämmel (ongoing, latest 5th) is really cool, a continuation of greenhorn-targeted Functional Programming Fundamentals by Erik Meijer (of which I recommend The Countdown Problem by Graham Hutton), see also A Quick Tour of Scala
- Effective ML by Yaron Minsky (effective with people, i.e. elegant)
- Monadic Design Patterns for the Web by Greg Meredith (ongoing, latest 3rd) is a monad tutorial like no other, see also Whiteboard Jam Session with Brian Beckman and Greg Meredith - Monads and Coordinate Systems (about zippers)
- The Catsters (you might need to look up natural isomorphism first if you'd be confused with the notion of "isomorphism of functors")
- Introduction to Algorithms by Erik Demaine and Charles Leiserson at MIT (elementary, but high quality and good selection of topics, I only missed a lecture about priority queues -- here NPTEL at IIT Delhi, also a lecture on tries -- a lecture on Fibonacci Heaps would be great)
- Graphical Models and Variational Methods (also from Berder Island) by Christopher Bishop (the relevant part of Pattern Recognition and Machine Learning starts at chapter 8)
- Modern Physics: Statistical Mechanics by Leonard Susskind at Stanford (slow and sometimes handwavy, nice introduction of probability and entropy, nice treatment of phase transitions on "spin lattices")
- Linear Dynamical Systems, then Convex Optimization by Stephen Boyd at Stanford University
- Mining Sets of Patterns by Jilles Vreeken, Siegfried Nijssen, Bjorn Bringmann; here are slides to download (slides are not shown in the video and aren't synchronized)
- Cognitive Science C102: Scientific Approaches to Consciousness at UC Berkeley (rather trivial, mostly cursory overview of experimental results, ongoing); podcasts about consciousness:
- Consciousness with Christof Koch (BSP 22)
- Review: "On Being Certain" (BSP 42)
- Meditation and the Brain with Daniel Siegel (BSP 44)
- How our Brain Creates Our World with Chris Frith, PhD (BSP 57)
- Interview with Philosopher Alva Noë (BSP 58) (has written a book " Out of Our Heads: Why You Are Not Your Brain, and Other Lessons from the Biology of Consciousness")
- Affective Neuroscience with Jaak Panksepp (BSP 65)
- Thomas Metzinger explores Consciousness (BSP 67)
- Embodied Cognition with Lawrence Shapiro (BSP 73)
- CPBD 082: Eric Schwitzgebel – The Unreliability of Naive Introspection
- CPBD 085: Marcel Brass – The Neuroscience of Free Will
- [I'll add more later as I watch or recall some]
Candidates:
- perhaps Graphical models by Zoubin Ghahramani (but it's redundant with Chris Bishop's)
- yet more candidates from videolectures.net:
- Dynamical Logic by Peter H. Schmitt
- Unsupervised learning by Dale Schuurmans
- Bayesian inference and Gaussian processes by Carl Edward Rasmussen
- Introduction to Modal Logic by Rajeev P. Goré
- Introduction to Robotics at Stanford
- Underactuated Robotics at MIT
- Skiena's Programming Challenges Lectures
- Introduction to Psychology with Paul Bloom at Yale or Psychology 1 - General Psychology at UC Berkeley
- Evolution, Ecology and Behavior with Stephen C. Stearns at Yale
- ...
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