Machine Learning , Foundations Spring Semester , 2003 / 4 Lecture 8 : Artificial neural networks

Theoretical Foundations of Machine Learning Fall 2015 Lecture 15 : Neural Networks Theory

EECS 598 - 005 : Theoretical Foundations of Machine Learning Fall 2015 Lecture 15 : Neural Networks Theory

Machine Learning Spring semester 2004 Lecture 3 : March 15 , 2004

This lecture summary discusses the foundations on which PAC Learning, a machine learning model, is based on. This model focuses on inductively learning an unknown target function, given only training examples of this target function and a space of candidate hypotheses. The questions that we will concern are how many training examples are sufficient to successfully learn the target function, and...

The Boltzmann Machine as a Neural Model

1. Hinton, Geo rey E., Terrence J. Sejnowski and David H. Ackley, Boltzmann Machines: Constraint Satisfaction Networks that Learn, Technical Report CMU-CS-84-119, May 1984. 2. Simpson, Patrick K., Arti cial Neural Systems: Foundations, Paradigms, Applications, and Implementations, 1990. Chapter 5: ANS Paradigms and Their Applications and Implementations, pages 120-127. 3. Hinton, Geo rey, Lectu...

Computational Genomics Fall Semester , 2003 Lecture 8 : December 17 , 2003

Our goal is to model biological processes given biological data. It is essential for such a model to be of probabilistic nature, since measurements are noisy and molecular biology itself has stochastic characteristics. In this lecture Bayesian networks [4, 8] will be introduced , these provide graphical representation of joint probability distributions in a compact way. These networks are usefu...