ALGORITHMIC METHODS AND MACHINE LEARNING
Corso
A Padova
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Descrizione
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Tipologia
Corso
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Luogo
Padova
Written exam and (individual) project. The project is due by the end of the course.
Sedi e date
Luogo
Inizio del corso
Inizio del corso
Opinioni
Materie
- E-learning
Programma
- Algorithmic Methods:
Preliminaries: definition of problem, instance, solution, algorithm. Models of computation. Analysis of algorithms: correctness and running time,. Asymptotic analysis.
Basic data structures: lists, stacks, queues. Trees and their properties. Dictionaries and their implementation. Priority queues.
Graphs: representation of graphs. Basic properties. Graph searches and applications.
Divide and Conquer paradigm: the use of recursion. Recurrence relations. Case study: sorting.
Dynamic programming: coping with repeating subproblems. Memoization of recursive code. Case study: optimization algorithms on sequences.
Greedy paradigm: solving by successive choices. Applicability of the paradigm. Case study: data compression.
- Machine Learning
Introduction to Machine Learning: why machine learning is useful; when to use it.; where to use it; Machine Learning paradigms; basic ingredients of Machine Learning; complexity of the hypothesis space; complexity measures; examples of supervised learning algorithms.
Application Issues: classification pipeline, representation and selection of categorical variables; model selection, evaluation measures.
in Depth (theory and practice using Python and Scikit-Learn): Support Vector Machines; Decision Trees and Random Forest; Neural Networks and Deep Learning; Manifold Learning; Kernel Density Estimation.
Hai bisogno di un coach per la formazione?
Ti aiuterà a confrontare vari corsi e trovare l'offerta formativa più conveniente.
ALGORITHMIC METHODS AND MACHINE LEARNING