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A survey of machine learning techniques: a hitchhiker's guide to classification, regression, clustering and deep learning models
This project consists of a survey regarding the major flavors of machine learning, i.e. classification, regression and clustering models. It also contains a complimentary research about deep learning, in order to elucidate concepts used by more sophisticated models. ...learn more
Project status: Concept
Overview / Usage
Firstly we plan to make a survey regarding the major flavors of Machine Learning. That would include study and research on classic classification, regression and clustering models and algorithms. The knowledge acquired in this phase could then be presented to our fellow colleagues in workshop sessions and/or talks. Then, we would proceed to a complimentary research about Deep Learning, in order to understand and build more sophisticated models. This last mostly-theoretic part would be followed by hands-on projects, which we plan to run on Intel AI DevCloud environment and then write papers reporting our results and insights.
Methodology / Approach
Firstly we plan to make a survey regarding the major flavors of Machine Learning. That would include study and research on classic classification (probabilistic models, neural networks, decision trees and support vector machines), regression (linear, polynomial and logistic) and clustering (distribution, centroid, connectivity and density-based) models and algorithms. The knowledge acquired in this phase could then be presented to our fellow colleagues in workshop sessions and/or talks. Then, we would proceed to a complimentary research about Deep Learning, in order to understand and build more sophisticated models (convolutional networks)). This last mostly-theoretic part would be followed by hands-on projects, which we plan to run on Intel AI DevCloud environment and then write papers reporting our results and insights.
Technologies Used
Git and LaTeX.
