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Machine Learning

Descrizione

COURSE OBJECTIVES

Students will learn how to design linear and nonlinear models for regression, prediction and classification as well as assess their performance in a sound way. At the same time, students will learn how to use deep learning architectures and learning algorithms in key real-world applications.

 

COURSE DESCRIPTION

The course will address the following topics: Supervised learning: linear and nonlinear models for regression and prediction, statistical theory of learning, feature extraction and model selection. Deep learning: architectures including autoencoders, convolutional neural networks and learning procedures. Model performance assessment: cross validation, k-fold cross validation, leave-one-out, bootstrap. Unsupervised learning: K-means clustering, fuzzy C-means, principal component analysis.

 

LEARNING METHODS

Lab sessions will focus on practical aspects and show how to design an appropriate machine learning solution to real-world problems. Basics in Calculus, Probability and Statics are requested

 

EXAMINATION INFORMATION
Assignments and exam.

 

PREREQUISITES

  • Calculus
  • Linear Algebra
  • Probability & Statistics
  • Programming Fundamentals 1
  • Software Atelier 1: Fundamentals of Informatics

 

REFERENCES

  •  T.Hastie, R.Tibshirani, J.Friedman, The elements of statistical learning, Springer
  • Slides and material provided by the professor.

Persone

 

Alippi C.

Docente titolare del corso

Informazioni aggiuntive

Semestre
Primaverile
Anno accademico
2021-2022
ECTS
6
Lingua
Inglese
Offerta formativa
Bachelor of Science in Informatics, Corso a scelta, 3° anno
Master of Science in Economics in Finance, Corso obbligatorio, Minor in Digital Finance, 1° anno