MACHINE LEARNING FOR PATTERN RECOGNITION (2st MODULE)
cod. 1006079

Academic year 2022/23
1° year of course - First semester
Professor
CAGNONI Stefano
Academic discipline
Sistemi di elaborazione delle informazioni (ING-INF/05)
Field
Attività formative affini o integrative
Type of training activity
Related/supplementary
24 hours
of face-to-face activities
3 credits
hub: PARMA
course unit
in ENGLISH

Integrated course unit module: MACHINE LEARNING FOR PATTERN RECOGNITION

Learning objectives

The objective of this module is to provide students with the theoretical basis and practical knowledge of some relevant machine-learning algorithms, aimed at classifying data.

The methods described in the course will allow students to:
- learn and use inductive-learning algorithms
- learn and use neural nets and other algorithm classes for the supervised classification of data
- learn and use the main supervised and unsupervised clustering algorithms

Prerequisites

Entry-level courses in linear algebra and probability theory, such as those normally offered in the corresponding 3-year Laurea course, are necessary pre-requisites for this course.

Course unit content

Part 1: Introduction

Lesson 1: How to set up a machine learning experiment
Lesson 2: Learning-based classification

Part 2: Neural networks

Lesson 3: Introduction to neural networks
Lesson 4: Supervised and unsupervised learning
Lesson 5: Supervised learning: the Backpropagation algorithm
Lesson 6: Unsupervised learning and clustering
Lesson 7: Kohonen's self-organizing maps (SOM)
Lesson 8: Learning Vector Quantization

Part 3: Other learning-based classifiers

Lesson 9: Support Vector Machines


Labs:

Lab 1: WEKA
Lab 2: Classifiers in WEKA: Multi-Layer Perceptrons
Lab 3: SOM-based clustering

Full programme

- - -

Bibliography

[1] C. W. Therrien, "Decision, estimation and classification" Wiley, 1989
[2] C. M. Bishop "Pattern Recognition and Machine Learning", Springer, 2006.
[3] R O Duda, P, E. Hart, D. G. Stork, "Pattern classification", 2nd Ed., Wiley, 2001

Teaching methods

Classroom teaching, 18 hours.
Labs, 6 hours.

Homework regularly assigned.

Assessment methods and criteria

A practical project will be assigned, consisting of the development of an application using the methods taught during the course, whose results will be presented and discussed by the student both as a written report and as an oral presentation.

Other information

Office Hours

By appointment (Scientific Complex, Building 1, floor 2, email stefano.cagnoni[AT]unipr.it).