CODE | 52507 |
---|---|
ACADEMIC YEAR | 2018/2019 |
CREDITS | 6 credits during the 3nd year of 8766 Mathematical Statistics and Data Management (L-35) GENOVA |
SCIENTIFIC DISCIPLINARY SECTOR | SECS-S/01 |
LANGUAGE | Italian |
TEACHING LOCATION | GENOVA (Mathematical Statistics and Data Management) |
SEMESTER | 2° Semester |
TEACHING MATERIALS | AULAWEB |
Provide the students with the basic skills for extracting knowledge from large data sets.
Develop the basic skills for extracting knowledge and knowledge from large data sets, in particular by forming an
At the end of the course students will
Combination of traditional lectures and lab sessions
First part: introduction to aata mining and applications in fraud detection
Introduction to Data Mining, Data science and big data analytics
Main techniques
The Data Mining Process - CRISP
Seven Class of Algorithms
Supervised Learning – Classification
Unsupervised Learnimg – Clustering
Outliers detection
Regression
Reinforced Learning
Ranking
Deep Learning
Top ten data mining algorithms
Examples and application using WEKA
Application to marketing, finance and medicine
Big Data and Hadoop
The NOSql paradigm
Second part: Machine Learning Algorithms for Data mining
Introduction to Data Mining and Machine Learning.
Taxonomy of the Data Mining problems
Statistical Inference
Support Vector Machines (extension to kernels)
Support Vector Regression (extension to kernels)
K-means and Spectral Clustering
Decision Trees and Random Forests
Model Selection and Error Estimation
Office hours: By appointment arranged by email with Luca Oneto luca.oneto@unige.it and Fabrizio Malfanti <fabrizio.malfanti@intelligrate.it> For organizational issues contact by email Eva Riccomagno <riccomagno@dima.unige.it>
FABRIZIO MALFANTI (President)
EVA RICCOMAGNO (President)
LUCA ONETO
Combination of traditional lectures and lab sessions
The class will start according to the academic calendar.
To take the exam, you must sign up online.
The examination of the first part consists of the discussion of a group project on a topic agreed with the lecturer and of a written examination on which the oral examination can be based.
The examination of the second part consists of the discussion of a project on a topic agreed with the lecturer and developed autonomously by the student.
The final mark is the weighted average of the marks of the two parts with weights the number of ECTS of each part, namely 3 ECTS for each part.
The exam will check if the student has learned the methodologies and techniques for extracting knowledge from a big set of data through a small project which requires the solution of a real world data mining problem.
Date | Time | Location | Type | Notes |
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21/01/2019 | 09:00 | GENOVA | registrazione | per gli studenti iscritti all'insegnamento nell'a.a.2017/18 e precedenti |
30/05/2019 | 09:00 | GENOVA | Laboratorio | |
20/06/2019 | 09:00 | GENOVA | Laboratorio | |
23/07/2019 | 09:00 | GENOVA | Laboratorio |
By appointment arranged by email with Luca Oneto luca.oneto@unige.it and Fabrizio Malfanti <fabrizio.malfanti@intelligrate.it>
For organizational issues contact by email Eva Riccomagno <riccomagno@dima.unige.it>
The web page of the second part of the course is https://sites.google.com/view/lucaoneto/teaching/dm-smid