Machine Learning: Generative and Discriminative Models

• Gaussians, Naïve Bayes, Mixtures of multinomials • Mixtures of Gaussians, Mixtures of experts, Hidden Markov Models (HMM) ... – by fitting Gaussian class-conditional densities will result in . 2M . parameters for means, M(M+1)/2 ... Markov Random Field (MRF)

  Field, Mixtures, Random, Gaussian, Markov, Markov random field

Machine Learning Basics: Estimators, Bias and Variance

Machine Learning Basics: Estimators, Bias and Variance Sargur N. Srihari srihari@cedar.buffalo.edu ... • To distinguish estimates of parameters from their true value, a point estimate of a parameter θ is represented by • Let {x(1), x(2),..x(m)} be m independent and

  Machine, Parameters

The Hessian Matrix - University at Buffalo

Diagonal Approximation • In many case inverse of Hessian is needed • If Hessian is approximated by a diagonal matrix (i.e., off-diagonal elements are zero), its inverse is trivially computed • Complexity is O(W) rather than O(W2) for full Hessian 7

  Matrix, Diagonal, Diagonal matrix

Backpropagation - University at Buffalo

Machine Learning Srihari Matrix Multiplication: Forward Propagation •Each layer is a function of layer that preceded it •First layer is given by z =h(W(1)T x +b(1)) •Second layer is y = σ(W(2)T x +b(2)) •Note that W is a matrix rather than a vector

Machine Learning Basics: Supervised Learning Algorithms

Deep Learning Probabilistic Supervised Classification Srihari • If we only have two classes we only need to specify the distribution for one of these classes – The probability of the other class is known – Linear regression has a closed-form solution – But …

  Learning

Gaussian Distribution - Welcome to CEDAR

• For a multivariate Gaussian distribution N(x| µ,Λ-1) for a D-dimensional variable x – Conjugate prior for mean µ assuming known precision is Gaussian – For known mean and unknown precision matrix Λ, conjugate prior is Wishart distribution – If both mean and precision are unknown conjugate prior is Gaussian-Wishart

  Distribution, Multivariate, Gaussian, Gaussian distribution, Multivariate gaussian distributions

Gaussian Derivatives - University at Buffalo

• Slice the surface with horizontal planes which the locus of points with the quadratic form. INFORMATION THEORY. Relative Entropy. ... great generality, and it is useful when we seek to know whether something other than the assumed case …

  Panels, Great

Iterative Reweighted Least Squares

•Derivative has the form •Setting equal to zero and solving we get Machine Learning Srihari 4 y(x,w)=w j ... 3.Gradient 4.Hessian 5.Newton-Raphsonupdate …

  Derivatives

Chapter 1 Introduction Linear Models and Regression Analysis

The term reflects the stochastic nature of the relationship ... Different statistical estimation procedures, e.g., method of maximum likelihood, principal of least squares, ... then logistic regression is used. If all explanatory variables are qualitative, then analysis of variance technique is used. If some

  Logistics, Regression, Stochastic, Likelihood, Logistic regression

Understanding the difficulty of training deep feedforward ...

layer, and with a softmax logistic regression for the out-put layer. The cost function is the negative log-likelihood −logP(y|x),where(x,y)isthe(inputimage,targetclass) pair. The neural networks were optimized with stochastic back-propagation on mini-batches of size ten, i.e., the av-erage g of ∂−logP(y|x) ∂θ was computed over 10 ...

  Network, Logistics, Regression, Neural network, Neural, Stochastic, Likelihood, Logistic regression

Bilinear CNN Models for Fine-grained Visual Recognition

the classification function C we use logistic regression or linear SVM. This can be replaced with a multi-layer neural network if non-linearity is desirable. End-to-end training Since the overall architecture is a directed acyclic graph the parameters can be trained by back-propagating the gradients of the classification loss

  Logistics, Fine, Regression, Logistic regression

An Introduction to Generalized

14.2 Binary variables and logistic regression 267 14.3 Nominal logistic regression 271 14.4 Latent variable model 272 14.5 Survival analysis 275 14.6 Random effects 277 14.7 Longitudinal data analysis 279 14.8 Some practical tips for WinBUGS 286

  Logistics, Regression, Logistic regression

Link Prediction Based on Graph Neural Networks

heuristics’ combination. For example, the path ranking algorithm [28] trains logistic regression on different path types’ probabilities to predict relations in knowledge graphs. Nickel et al. [23] propose to incorporate heuristic features into tensor factorization models. However, these models still …

  Logistics, Regression, Logistic regression

Theory of Deep Learning - Princeton University

Contents 1 Basic Setup and some math notions 11 1.1 List of useful math facts 12 1.1.1 Probability tools 12 1.1.2 Singular Value Decomposition 13 2 Basics of Optimization 15 2.1 Gradient descent 15 2.1.1 Formalizing the Taylor Expansion 16 2.1.2 Descent lemma for gradient descent 16 2.2 Stochastic gradient descent 17 2.3 Accelerated Gradient Descent 17 2.4 Local Runtime …

  Learning, Theory, Deep, Stochastic, Theory of deep learning

[CM] Choice Models - Stata

Iteration 0: log likelihood = -249.36629 Iteration 1: log likelihood = -236.01608 Iteration 2: log likelihood = -235.65162 Iteration 3: log likelihood = -235.65065 Iteration 4: log likelihood = -235.65065 Conditional logit choice model Number of obs = 840 Case ID variable: id Number of cases = 210 Alternatives variable: mode Alts per case: min = 4

  Model, Choice, Likelihood, Choice models