Andres Mendez-Vazquez

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Andres Mendez-Vazquez has participated in several projects for the USA Army, Mexican Air Force, Oracle MDC, IBM Mexico, and several startups. As a professional of Machine Learning and Artificial Intelligence, his main task is to propose, design, develop and program algorithms for specific solutions on those areas. Additionally, he is an associate research professor at Cinvestav Guadalajara where he leads a Machine Learning Research Group. He got his PhD in Computer Engineering doing Machine Learning at the University of Florida, USA . His research and interests are in applying, designing and developing Machine Learning, Deep Learning and Computational Mathematics algorithms.

machine learning analysis of algorithms data structures artificial intelligence linear algebra algorithms neural networks complexity deep learning bayesian networks probability map bayesian estimation automatic differentiation backpropagation company profiles reinforcement learning deep heuristic search tensors data sciences unsupervised learning clustering k-means learning boosting statistical learning risk functional loss functions function approximation feature selection convex optimization data analytics semi-supervised unsupurvised supervised data mining logistic regression markov chains monte carlo math for intelligent systems b-trees divide and conquer skip list genetic algorithms schema theorem amortized analysis binary trees hash tables search median statistics linear sorting sorting dual problem lagrangian's

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Andres Mendez-Vazquez

2.03 bayesian estimation

05 linear transformations

01.04 orthonormal basis_eigen_vectors

01.03 squared matrices_and_other_issues

01.02 linear equations

01.01 vector spaces

06 recurrent neural_networks

05 backpropagation automatic_differentiation

01 Introduction to Neural Networks and Deep Learning

25 introduction reinforcement_learning

20 k-means, k-center, k-meoids and variations

18.1 combining models

17 vapnik chervonenkis dimension

Schedule for Data Lab Community Path in Machine Learning

Introduction to logistic regression

Markov chain monte_carlo_methods_for_machine_learning

Artificial Intelligence 06.3 Bayesian Networks - Belief Propagation - Junction Trees

Artificial Intelligence 06.2 More on Causality Bayesian Networks

Artificial Intelligence 06.01 introduction bayesian_networks

Artificial Intelligence 02 uninformed search