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Syllabus and Course Schedule Time and Location: Monday, Wednesday 4:30-5:50pm, Bishop Auditorium Class Videos: Current quarter's class videos are available here for SCPD students and here for non-SCPD students.
Andrew Y. Ng Advice for applying Machine Learning Andrew Ng Stanford University Andrew Y. Ng Today’s Lecture • Advice on how getting learning algorithms to different applications. • Most of today’s material is not very mathematical. But it’s also some of the hardest material in this class to understand. • Some of what I’ll say today is debatable. • Some of what I’ll say is not good advice for doin
Linear Algebra Review and Reference Zico Kolter (updated by Chuong Do) September 30, 2015 Contents 1 Basic Concepts and Notation 2 1.1 Basic Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 Matrix Multiplication 3 2.1 Vector-Vector Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Matrix-Vector Products . . . . . . . . . . . . . . . . . . .
CS 229 Machine Learning Final Projects, Autumn 2014 Nonlinear Reconstruction of Genetic Networks Implicated in AML.Aaron Goebel, Mihir Mongia .[pdf] Can Machines Learn Genres.Aaron Kravitz, Eliza Lupone, Ryan Diaz.[pdf] Identifying Gender From Facial Features.Abhimanyu Bannerjee, Asha Chigurupati.[pdf] Equation to LaTeX.Abhinav Rastogi, Sevy Harris.[pdf] Intensity prediction using DYFI.Abhineet Gu
This version: December 12, 2013 Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks Lawrence Takeuchi * ltakeuch@stanford.edu Yu-Ying (Albert) Lee yy.albert.lee@gmail.com Abstract We use an autoencoder composed of stacked restricted Boltzmann machines to extract features from the history of individual stock prices. Our model is able to discover an en- hanced version of the mome
CS 229 Machine Learning Final Projects, Autumn 2012 A Facebook Profile-Based TV Recommender System. Jeff David, Samir Bajaj, Cherif Jazra. [pdf] A Flexible System for Hand Gesture Recognition. Matt Vitelli, Dominic Becker, Laza Upatising. [pdf] A New Rival To Predator And ALIEN. Martin Raison, Botao Hu. [pdf] A Risky Proposal: Designing a Risk Game Playing Agent. Juan Lozano, Dane Bratz. [pdf] A S
Course Description 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, practica
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