CS229 Lecture notes Andrew Ng Mixtures of Gaussians and the EM algorithm In this set of notes, we discuss the EM (Expectation-Maximization) algorithm for density estimation. Suppose that we are given a training set {x(1), . . . , x(n)} as usual. Since we are in the unsupervised learning setting, these points do not come […]
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UNSUPERVISED LEARNING TODAY K MEANS Mixtureof Qaussians Em tt Supervised Seitwg Unsupervised Is HARDER than supervises TECHNIQUES a Unsupervised Nolabels allow stronger ASSUMPTIONS accept weaker GUARANTEES IDEAS Are VALUABLE K MEANS Given Given X X EIRD Integer K alcluster K 2 Ia 1 De e.g de o find Assignment of X to ONE al k
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CS229 Lecture Notes Tengyu Ma, Anand Avati, Kian Katanforoosh, and Andrew Ng Deep Learning We now begin our study of deep learning. In this set of notes, we give an overview of neural networks, discuss vectorization and discuss training neural networks with backpropagation. 1 Supervised Learning with Non-linear Mod- els In the supervised learning setting
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[06-30213][06-30241][06-25024] Computer Vision and Imaging & Robot Vision Dr Hyung Jin Chang Dr Yixing Gao h.j.chang@bham.ac.uk y.gao.8@bham.ac.uk School of Computer Science STRUCTURE FROM MOTION (SFM) A BRIEF OVERVIEW The aim of SFM • Givenseveralimagesofsamescene • Reconstructthecamerapositionsandreconstruct the 3D scene • Assumeapartially-calibratedcase,inwhichthe camera calibration matrices 𝑲 are known. Xiao, J. Multiview 3D Reconstruction for Dummies SFM
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COMP9318 Review Yifang Sun @ UNSW April 22, 2021 Data Warehousing and OLAP Understand the four characteristics of DW (DW vs. Data Mart) Differences between OLTP and OLAP Multidimensional data model; data cube; fact, dimension, measure, hierarchies cuboid, cube lattice three types of schemas four typical OLAP operations
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CS229 Lecture notes Andrew Ng The k-means clustering algorithm In the clustering problem, we are given a training set {x(1), . . . , x(n)}, and want to group the data into a few cohesive “clusters.” Here, x(i) ∈ Rd as usual; but no labels y(i) are given. So, this is an unsupervised learning problem.
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COMP9318: Data Warehousing and Data Mining — L7: Classification and Prediction — Data Mining: Concepts and Techniques 1 n Problem definition and preliminaries Data Mining: Concepts and Techniques 2 ML Map Data Mining: Concepts and Techniques 3 Classification vs. Prediction n Classification: n predicts categorical class labels (discrete or nominal) n classifies data (constructs a
Part X CS229 Lecture notes Andrew Ng Factor analysis When we have data x(i) ∈ Rd that comes from a mixture of several Gaussians, the EM algorithm can be applied to fit a mixture model. In this setting, we usually imagine problems where we have sufficient data to be able to discern the multiple-Gaussian structure
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[06-30213][06-30241][06-25024] Computer Vision and Imaging & Robot Vision Dr Hyung Jin Chang Dr Yixing Gao h.j.chang@bham.ac.uk y.gao.8@bham.ac.uk School of Computer Science FITTING: VOTING AND THE HOUGH TRANSFORM (SZELISKI 4.3.2) Now: Fitting • Want to associate a model with multiple observed features [Fig from Marszalek & Schmid, 2007] For example, the model could be a line,
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Part XI CS229 Lecture notes Andrew Ng Principal components analysis In our discussion of factor analysis, we gave a way to model data x ∈ Rd as “approximately” lying in some k-dimension subspace, where k ≪ d. Specifi- cally, we imagined that each point x(i) was created by first generating some z(i) lying in the
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