PPT-Gaussians

Pieter Abbeel UC Berkeley EECS Many slides adapted from Thrun Burgard and Fox Probabilistic Robotics TexPoint fonts used in EMF Read the TexPoint manual before you delete this box . Current techniques for learning such mixtures from data are local search heuris tics with weak performance guarantees We present the 64257rst provably correct algorithm for learning a mixture of Gaus sians This algorithm is very simple and returns t Unsupervised. Learning. Santosh . Vempala. , Georgia Tech. Unsupervised learning. Data is no longer the constraint in many settings. . … (imagine sophisticated images here)…. But, . How to understand it? . Acoustic models in Kaldi . Support for standard ML-trained models. Linear transforms like LDA, HLDA, MLLT/STC. Speaker adaptation with fMLLR, MLLR. Support for tied-mixture systems initially discussed. from his textbook: ”Pattern Recognition and Machine Learning”. Overview. Clustering with K-means and a proof of convergence that uses energies.. Clustering with a mixture of Gaussians and a proof of convergence that uses free energies. Tutorial.. Session 2.. SIFT. Gonzalo . Vaca-Castano. Sift purpose. Find and describe interest points invariants to:. Scale. Rotation. Illumination. Viewpoint. Do it Yourself. Constructing a scale . space. Janaka. CDA 6938. What is Background Subtraction?. Identify foreground pixels. Preprocessing. step for most vision algorithms. Applications. Vehicle Speed Computation from Video. Why is it Hard?. Naïve Method |. Pieter . Abbeel. UC Berkeley EECS. Many . slides adapted from . Thrun. , . Burgard. and Fox, Probabilistic Robotics. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . Winter 2012. Daniel Weld. Slides adapted from Carlos . Guestrin. , Dan Klein & Luke . Zettlemoyer. Machine Learning. 2. Supervised Learning. Parametric. Reinforcement Learning. Unsupervised Learning. SIFT. Gonzalo . Vaca-Castano. Sift purpose. Find and describe interest points invariants to:. Scale. Rotation. Illumination. Viewpoint. Do it Yourself. Constructing a scale . space. LoG. . Approximation. the . EM Algorithm. CSE . 6363 – Machine Learning. Vassilis. . Athitsos. Computer Science and Engineering Department. University of Texas at . Arlington. 1. Gaussians. A popular way to estimate . probability density . Mark Hasegawa-Johnson. 9/24/2018. Contents. Gaussian pdf; Central limit theorem, Brownian motion. White Noise. Vector of . i.i.d. . Gaussians. Vector of Gaussians that are independent but not identical. Zeeshan. Ali . Sayyed. What is State Estimation?. We need to estimate the state of not just the robot itself, but also of objects which are moving in the robot’s environment.. For instance, other cars, people, . from a. Broad Class of Distributions. Vadim Lyubashevsky and Daniel . Wichs. Trapdoor Sampling. A. t. s. =. Given: a random matrix . A. and vector . t. Find: vector . s. with small coefficients such that . Learning. Santosh . Vempala. , Georgia Tech. Unsupervised learning. Data is no longer the constraint in many settings. . … (imagine sophisticated images here)…. But, . How to understand it? .