MACHINE LEARNING

Università degli Studi di Padova.

Corso

A Padova

6001-7000 €

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049 827 3311-3312

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Descrizione

  • Tipologia

    Corso

  • Luogo

    Padova

The evaluation of the acquired skills and knowledge will be performed using two contributions:

1. A written exam without the book, where the student must solve few problems, with the aim of verifying the acquisition of the main ingredients of a learning problem and of the main machine learning tools, the analytical ability to use these tools and the ability to interpret the typical results of a practical machine learning problem.

2. Computer simulations (optional) with the aim of acquiring the practical competences for using machine learning tools. These simulations, to be performed at home, allow to verify the ability of practically exploiting the acquired theoretical concepts. The student will have to provide a brief document explaining the employed methodologies used to solve the assigned problem together with the obtained results.

The final grade will be based on the written test with a bonus up to 3 point for the students who will hand in also the lab assignments.

Sedi e date

Luogo

Inizio del corso

Padova
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Riviera Tito Livio, 6, 35122

Inizio del corso

Consultare

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Opinioni

Materie

  • E-learning

Programma

Motivation; components of the learning problem and applications of Machine Learning. Supervised and unsupervised learning.

PART I: Supervised Learning

1. Introduction: Data, Classes of models, Losses.

2. Probabilistic models and assumptions on the data. The regression function. Regression and Classification.

3. When is a model good? Model complexity, bias variance tradeoff/generalization (VC dimension, generalization error).

4. Models for Regression: Linear Regression (scalar and multivariate), subset selection, linear-in-the-parameters models, regularization.

5. Classes of nonlinear models: Sigmoids, Neural Networks.

6. Kernel Methods: SVM.

7. Models for Classification: Logistic Regression, Neural Networks, Perceptron, Naïve Bayes Classifier, SVM, Deep Learning.

8. Validation and Model Selection: Generalization Error, Bias-Variance Tradeoff, Cross Validation. Model complexity determination.

PART II: Unsupervised learning

1. Cluster analysis: K-means Clustering, Mixtures of Gaussians and the EM estimation.

2. Dimensionality reduction: Principal Component Analysis (PCA).

Categorie correlate

Chiama il centro

049 827 3311-3312

Hai bisogno di un coach per la formazione?

Ti aiuterà a confrontare vari corsi e trovare l'offerta formativa più conveniente.

MACHINE LEARNING

6001-7000 €

I migliori corsi di Analisi e sviluppo

Vedi tutti

Vedi tutti