18 Data Streams
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Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. In in this lecture we overview the mining of data streams
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11 Settembre 2021 - Giocare con i Videogiochi
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Data Driven Game Design @ Campus Party 2018
Data Driven Game Design @ Campus Party 2018Pier Luca Lanzi Prof. Pier Luca Lanzi discusses using data-driven game design and machine learning techniques like player modeling and gameplay analysis tools to balance multiplayer first-person shooters. He proposes using the distribution of kills and scores among players as a proxy to evaluate balancing. His research also looks at using AI to automatically design game maps and levels to improve balancing, as well as generative adversarial networks to generate new Doom levels.
GGJ18 al Politecnico di Milano - Presentazione che precede la presentazione d...
GGJ18 al Politecnico di Milano - Presentazione che precede la presentazione d...Pier Luca Lanzi Presentazione che precede la presentazione del tema della Global Game Jam 2018
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DMTM Lecture 20 Data preparationPier Luca Lanzi Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 19 Data exploration
DMTM Lecture 19 Data explorationPier Luca Lanzi Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 18 Graph mining
DMTM Lecture 18 Graph miningPier Luca Lanzi Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 17 Text mining
DMTM Lecture 17 Text miningPier Luca Lanzi Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 16 Association rules
DMTM Lecture 16 Association rulesPier Luca Lanzi Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 15 Clustering evaluation
DMTM Lecture 15 Clustering evaluationPier Luca Lanzi Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 14 Density based clustering
DMTM Lecture 14 Density based clusteringPier Luca Lanzi Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 13 Representative based clustering
DMTM Lecture 13 Representative based clusteringPier Luca Lanzi Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 12 Hierarchical clustering
DMTM Lecture 12 Hierarchical clusteringPier Luca Lanzi Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 11 Clustering
DMTM Lecture 11 ClusteringPier Luca Lanzi Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 10 Classification ensembles
DMTM Lecture 10 Classification ensemblesPier Luca Lanzi Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 09 Other classificationmethods
DMTM Lecture 09 Other classificationmethodsPier Luca Lanzi This document discusses Naive Bayes classifiers and k-nearest neighbors (kNN) algorithms. It begins with an overview of Naive Bayes, including how it makes strong independence assumptions between attributes. Several examples are provided to illustrate Naive Bayes classification. The document then covers kNN, explaining that it is an instance-based learning method that classifies new examples based on their similarity to training examples. Parameters like the number of neighbors k and distance metrics are discussed.
DMTM Lecture 08 Classification rules
DMTM Lecture 08 Classification rulesPier Luca Lanzi Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
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