Difference between revisions of "Machine Learning Bio"
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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 Python language. | 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=== | ||
Revision as of 18:41, 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
