Unsupervised Learning
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The Concept of Unsupervised Learning, K-means Clustering Algorithm, K-means Algorithm, Mixtures of Gaussians and the EM Algorithm, Jensen's Inequality, The EM Algorithm, Summary
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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
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This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins);
Bayesian Statistics and Regularization, Online Learning, Advice for Applying Machine Learning Algorithms, Debugging/fixing Learning Algorithms, Diagnostics for Bias & Variance, Optimization Algorithm Diagnostics, Diagnostic Example - Autonomous Helicopter, Error Analysis, Getting Started on a Learning Problem
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Hawkins talks about how he started his first company while he was in college and the lessons he learnt from that experience. Real-world learning along with book and school type learning are instrumental in one's preparation to be an entrepreneur, he says.
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Advice for Applying Machine Learning, Debugging Reinforcement Learning (RL) Algorithm, Linear Quadratic Regularization (LQR), Differential Dynamic Programming (DDP), Kalman Filter & Linear Quadratic Gaussian (LQG), Predict/update Steps of Kalman Filter, Linear Quadratic Gaussian (LQG)
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Musk discusses the role that business school can play for an entrepreneur as well as the possibility of learning outside of school. According to him the, the important principle is to be dedicated to learning what you need to know - whether that be in school or empirically.
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Applications of Reinforcement Learning, Markov Decision Process (MDP), Defining Value & Policy Functions, Value Function, Optimal Value Function, Value Iteration, Policy Iteration
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Success is not how smart you are; it's how you can get people to do what you want. Learning to delegate and to build a team was one of the hardest lessons for Jeff Housenbold, the CEO of Shutterfly, to master early in his career. With the insight of hindsight, he now sees effective team leading as a critical step in learning the right balance between career and self, and keeping the ego and ambition in check.
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An Application of Supervised Learning - Autonomous Deriving, ALVINN, Linear Regression, Gradient Descent, Batch Gradient Descent, Stochastic Gradient Descent (Incremental Descent), Matrix Derivative Notation for Deriving Normal Equations, Derivation of Normal Equations
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How can we harness the emerging forms of interactive media to enhance the learning process? Professor Miyagawa and prominent guest speakers will explore a broad range of issues on new media and learning - technical, social, and business. Concrete examples of use of media will be presented as case studies. One major theme, though not the only one, is that today's youth,...more
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