Introduzione alla realizzazione di videogiochi - FunPier Luca Lanzi
Slide del corso "Introduzione alla realizzazione di videogiochi" tenuto per gli studenti delle scuole superiori presso la sede di Cremona del Politecnico di Milano.
Presentazione tenuta alla Biblioteca Civica di Thiene (VI) il 15 febbraio 2012 su "Software Libero" e Linux per conto di AVi LUG, il Linux User Group dell'Alto Vicentino e della .
http://www.avilug.it
Introduzione alla realizzazione di videogiochi - FunPier Luca Lanzi
Slide del corso "Introduzione alla realizzazione di videogiochi" tenuto per gli studenti delle scuole superiori presso la sede di Cremona del Politecnico di Milano.
Presentazione tenuta alla Biblioteca Civica di Thiene (VI) il 15 febbraio 2012 su "Software Libero" e Linux per conto di AVi LUG, il Linux User Group dell'Alto Vicentino e della .
http://www.avilug.it
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.