# Difference between revisions of "Machine Learning Bio"

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==Course Aim & Organization== | ==Course Aim & Organization== | ||

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− | The objective of this course is to give an advanced presentation, i.e., a statistical perspective, of the techniques most used in artificial intelligence and machine learning for pattern recognition, knowledge discovery, and data analysis/modeling. The course will provide the basics of Regression, Classification, and Clustering with practical exercises using the | + | The objective of this course is to give an advanced presentation, i.e., a statistical perspective, of the techniques most used in artificial intelligence and machine learning for pattern recognition, knowledge discovery, and data analysis/modeling. The course will provide the basics of Regression, Classification, and Clustering with practical exercises using the Python language. |

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===Teachers=== | ===Teachers=== | ||

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* [http://www-bcf.usc.edu/~gareth/ISL/ An Introduction to Statistical Learning with Applications in R] by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani | * [http://www-bcf.usc.edu/~gareth/ISL/ An Introduction to Statistical Learning with Applications in R] by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani | ||

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The course is composed by a set of ex-cattedra lectures on specific techniques (e.g., linear regression, linear discriminant analysis, clustering, etc.). Supervised and unsupervised learning are discussed in the framework of classification and clustering problems. The course outline is: | The course is composed by a set of ex-cattedra lectures on specific techniques (e.g., linear regression, linear discriminant analysis, clustering, etc.). Supervised and unsupervised learning are discussed in the framework of classification and clustering problems. The course outline is: | ||

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* '''''Unsupervised Learning Techniques''''': the most common approaches to unsupervised learning are described mostly focusing on clustering techniques such as hierarchical clustering, k-means, k-medoids, Mixture of Gaussians, DBSCAN, Jarvis-Patrick, etc.; | * '''''Unsupervised Learning Techniques''''': the most common approaches to unsupervised learning are described mostly focusing on clustering techniques such as hierarchical clustering, k-means, k-medoids, Mixture of Gaussians, DBSCAN, Jarvis-Patrick, etc.; | ||

* '''''Model Validation and Selection''''': model validation and selection are orthogonal issues to all previous techniques; during the course their fundamentals are described and discussed in the framework of linear models for regression (e.g., AIC, BIC, cross-validation, etc. ). | * '''''Model Validation and Selection''''': model validation and selection are orthogonal issues to all previous techniques; during the course their fundamentals are described and discussed in the framework of linear models for regression (e.g., AIC, BIC, cross-validation, etc. ). | ||

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===Detailed course schedule=== | ===Detailed course schedule=== | ||

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+ | [...] WORK IN PROGRESS [...] | ||

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## Revision as of 18:40, 3 March 2020

The following are last minute news you should be aware of ;-)

* 03/03/2020: The course is going to start soon ...

## Contents

## Course Aim & Organization

The objective of this course is to give an advanced presentation, i.e., a statistical perspective, of the techniques most used in artificial intelligence and machine learning for pattern recognition, knowledge discovery, and data analysis/modeling. The course will provide the basics of Regression, Classification, and Clustering with practical exercises using the Python language.

### Teachers

The course is composed by a blending of lectures and exercises by the course teacher and a teaching assistant.

- Matteo Matteucci: the course teacher
- Marco Cannici: the teaching assistant

### Course Program

Techniques from machine and statistical learning are presented from a theoretical (i.e., based on statistics and information theory) and practical perspective through the descriptions of algorithms, the theory behind them, their implementation issues, and few examples from real applications. The course mostly follows the following book which is also available for download in pdf

- An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

### Detailed course schedule

[...] WORK IN PROGRESS [...]

### Course Evaluation

The course evaluation is composed by two parts:

- HW: Homework with exercises covering the whole program
- WE: A written examination covering the whole program