# MACHINE LEARNING FOR PATTERN RECOGNITION

## Learning outcomes of the course unit

Course Objectives ----------------- The objective of the course is to provide

the student with the ability to understand and apply the basic rules of

machine learning and, in particular: - to apply the most common

statistical tests in classification among different categories - to synthesize

the structure of the optimal classifier and analyze its error performance -

to apply the most common feature extraction methods from input data

- to apply the most common statistical estimators in machine learning

- to apply the most common clustering algorithms in unsupervised learning

- to use neural nets and other algorithm classes for the supervised classification of data-

The abilities in applying the above-mentioned knowledge are in particular in the:

- design and performance analysis of classifiers in machine

learning

- selection of the most appropriate features to discriminate input

categories

- selection of the most appropriate clustering algorithms in the

design of unsupervised classifiers

## Prerequisites

Pre-requisites:

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 contents summary

MODULE 1 (Bononi):

Basic probability refresher. Bayesian binary and M-ary classification.

MAP and Minimax classifiers. Performance and ROC.

Gaussian case and linear discriminant rules.

Bayesian estimation (regression).

Maximum likelihood, MMSE, MMAE estimators.

Linear suboptimal estimators. Supervised learning.

Generative versus discriminative approaches.

Plug-in learning.

Bayesian learning.

Minimum empirical risk learning. Nonparametric probability density estimation.

Linear data reduction for feature extraction.

MODULE 2 (Cagnoni):

Support Vector Machines.

Classifier evaluation techniques. Unsupervised classification and clustering.

K-means and Isodata algorithms Self-Organizing Maps

Learning Vector Quantization

Kohonen networks

## Course contents

SYLLABUS (EVERY CLASS 2 HOURS)

MODULE 1:

Lec. 1. Introduction

- Problem statement and definitions

- Examples of machine learning problems

- Glossary of equivalent terms in Radar detecton theory, hypothesis testing and machine learning

Lec. 2. Probability refresher

- Axioms, conditional probability, total probability law, Bayes law, double conditioning, chain rule, independence and conditional independence of events.

- Discrete random variables (RV): expectation, conditional expectation. Pairs of RVs. Sum rule. Iterated expectation. Vectors of RVs. An extended example.

Lec. 3. Probability refresher

- Random vectors:

expectation, covariance and its properties, spectral decomposition of covariance matrix, whitening.

- Continuous RV.

Parallels with discrete RVs. Functions of RVs. Mixed RVs. Continuous random vectors.

- Appendix: differentiation rules for vectors and matrices.

Lec. 4.

- Gaussian RVs and their linear transformations. Mahalanobis distance.

Classification:

- Bayesian prediction: introduction, loss function, conditional risk, argmin/argmax rules

- Bayes classification: introduction

Lec. 5. Classification

- 0/1 loss -> maximum a posteriori (MAP) classifier. Binary MAP. Decision regions.

- Classifier performance.

- Likelihood ratio tests and receiver operating curve (ROC)

- Minimax rule

Lec. 6. Classification

- Binary Gaussian classification

- Homoscedastic case: linear discriminant analysis

- Heteroscedastic case: Bhattacharrya bound

- Bayes classification with discrete features

- Classification with missing data (composite hypothesis testing)

Lec. 7. Estimation

- Bayesian estimation: introduction

- Quadratic loss: minimum mean square error (MMSE) estimator = regression curve

- L1 loss: minimum mean absolute error (MMAE) estimator

- 0/1 loss: MAP estimator, and maximum likelihood (ML) in uniform prior.

- Regression for vector Gaussian case

- ML estimation for Gaussian observations

Lec. 8. Estimation

- ML for multinomial

- Conjugate priors in MAP estimation

- Estimation accuracy and ML properties, Cramer Rao bounds.

Suboptimal (non Bayesian) estimation:

- LMMSE estimation (linear regression)

- LMMSE derivation with LDU decomposition

Lec. 9. Estimation

- LMMSE examples

- Generalized linear regression

- Example: polynomial regression

- Sample LMMSE

- Generalized sample LMMSE.

Lec. 10. Learning

- Supervised learning: introduction

- Generative vs discriminatie approaches

- Example: logistic model

- Plug-in learning

ML fitting of logistic model: logistic regression

Example: handwritten digit recognition.

- Bayesian Learning

Lec. 11.

Learning:

- Empirical risk minimization

Nonparametric density estimation:

- Parzen window estimator

- kNN estimator

Lec. 12. linear data reduction

- Principal component analysis (PCA)

- Fisher linear classifier

MODULE 2:

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

## Recommended readings

[1] C. W. Therrien, "Decision, estimation and classification" Wiley, 1989

[2] R. O. Duda, P. E. Hart, D. G. Stork, "Pattern classification", 2nd Ed., Wiley, 2001

[3] D. Barber "Bayesian Reasoning and Machine Learning" Cambridge University Press, 2012.

[4] C. M. Bishop "Pattern Recognition and Machine Learning", Springer, 2006.

[5] T. Hastie, R. Tibshirani, J.

Friedman, "The Elements of Statistical Learning: Data mining, inference, and prediction", Springer, 2008.

[6] Eibe Frank, Mark A. Hall, and Ian H. Witten (2016). The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016.

## Teaching methods

Teaching methods:

Classroom teaching, 42 hours. In-class problem solving, 6 hours.

Homeworks regularly assigned.

## Assessment methods and criteria

MODULE 1, Bononi:

Oral exam, to be scheduled on an individual basis. When ready, please contact the instructor by email at alberto.bononi[AT]unipr.

it and by specifying the requested date. The exam consists of solving

some exercises and explaining theoretical details connected with them, for a total time of about 1 hour. You can bring your summary of important formulas in an A4 sheet to consult if you so wish.

MODULE 2, Cagnoni:

A practical project will be assigned, whose results will be presented and discussed by the student both as a written report and as an oral

presentation.

## Other informations

FURTHER INFO:

Office Hours Bononi: Monday 11:30-13:30 (Scientific Complex, Building 2,

floor 2, Room 2/19T). Cagnoni: by appointment (Scientific Complex,

Building 1, floor 2, email cagnoni[AT]ce.unipr.it).

Course website with teaching material (lecture notes, videolectures, solved exercises):