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

Artificial Intelligence 02 Uninformed Search

Artificial Intelligence 01 introduction

28 Dealing with the NP Poblems: Exponential Search and Approximation Algorithms

31 Machine Learning Unsupervised Cluster Validity

28 Machine Learning Unsupervised Hierarchical Clustering

27 Machine Learning Unsupervised Measure Properties

26 Machine Learning Unsupervised Fuzzy C-Means

25 Machine Learning Unsupervised Learaning K-means K-centers

24 Machine Learning Combining Models - Ada Boost

23 Machine Learning Feature Generation

22 Machine Learning Feature Selection

18 Machine Learning Radial Basis Function Networks Forward Heuristics

17 Machine Learning Radial Basis Functions

16 Machine Learning Universal Approximation Multilayer Perceptron

15 Machine Learning Multilayer Perceptron

14 Machine Learning Single Layer Perceptron

13 Machine Learning Supervised Decision Trees

12 Machine Learning Supervised Hidden Markov Chains

11 Machine Learning Important Issues in Machine Learning

09 Machine Learning - Introduction Support Vector Machines