Ingegneria Informatica | MACHINE LEARNING AND BIG DATA ANALYTICS
Ingegneria Informatica MACHINE LEARNING AND BIG DATA ANALYTICS
|DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA|
|YEAR OF COURSE 2|
|YEAR OF DIDACTIC SYSTEM 2016|
|The course is aimed at providing the student with the theoretical, metodological and technological knowledge on machine learning and on the analysis of large data sets, including both traditional techniques and innovative paradigms such as Deep Learning.|
Knowledge and understanding
Paradigms of structural learning, statistical learning and neural learning. Unsupervised learning. Deep learning. Paradigms and tools for big data analysis.
Applied knowledge and understanding
Design and realization of solutions to learning and data analytics problem by integrating existing tools and tuning in an effective way their operating parameters.
|Introduction to Pattern Recognition and Machine Learning. The general structure of a data analytics system.|
Vectorial and structural data representation.
Conceptual inductive learning in structural domains. Decision trees and rule learning. The algorithms AQ and ID3.
Statistical learning. Theoretical foundations. Bayesian formulation. Bias and variance errors. The curse of dimensionality. The Nearest Neighbor classifier.
Feature reduction. Feature selection with greedy algorithms(forward selection, backward elimination). Feature reduction with Principal Component Analysis (PCA).
Linear Discriminant Analysis (LDA). Regularization techniques (ridge regression, lasso). Support Vector Machines (SVM). The kernel trick and its use for non linearly separable problems.
Unsupervised learning. Clustering. k-Means and Single-Linkage Clustering.
Learning with artificial neural networks. The Perceptron. Multi-Layer Perceptrons (MLP).
Clustering with neural networks. Self-Organizing Maps (SOM). Learning Vector Quantization (LVQ).
Deep learning with neural networks. Fundamental principles. Restricted Boltzmann Machines (RBM)
Learning multimedial data. Convolutional Neural Networks (CNN).
Technologies for Big Data analytics. The Map-Reduce paradigm. The Apache Hadoop framework. Data storage on distributed file systems. Map-Reduce in Hadoop. The Mapper. The Shuffle and Reduce operations. Streaming.
|THE COURSE CONTAINS THEORETICAL LECTURES, IN-CLASS EXERCITATIONS AND PRACTICAL LABORATORY EXERCITATIONS.|
|Verification of learning|
|The exam is composed by the discussion of a team projectwork (for 3-4 persons teams) and an oral interview. The discussion of the projectwork aims at evaluating the ability to build a simple application of the tools presented in the course to a problem assigned by the teacher, and includes a practical demonstration of the realized application, a presentation of a quantitative evaluation of the application performance and a description of the technical choices involved in its realization. The interview evaluates the level of the knowledge and understanding of the theoretical topics, together with the exposition ability of the candidate.|
|To be defined|
|The course language is English.|
BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2019-05-14]