## Pattern Recognition

A biennial course, part of the

BioSB Research School

**Lecturers**
- dr. ir. Perry Moerland (Academic Medical Center)
- prof. dr. ir. Marcel Reinders (Delft University of Technology)
- prof. dr. Lodewyk Wessels (Netherlands Cancer Institute)

**Course coordinator**:

## **Objectives**

After having followed this course, a student should have an overview of basic pattern recognition techniques and be able to recognize what method is most applicable to classification problems (s)he encounters in bioinformatics applications.

## **Target audience**

The course is aimed at PhD students with a background in bioinformatics, computer science or a related field; a working knowledge of basic statistics and linear algebra is assumed. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background.

## **Description**

Many problems in bioinformatics require classification: prediction of the class to which a certain object (i.e. a gene, protein, cell, patient, ?) belongs. This calls for algorithms that can assign the most likely label (discrete output) to an object, given one or more measurements on that object. For most interesting problems, the underlying physics are too complex to explicitly formulate such an algorithm. In such cases, a machine learning approach is taken: an algorithm is constructed, with parameters that are tuned based on an available dataset of training examples. The algorithm should predict the labels for these examples as well as possible, yet still generalize, i.e. perform well on objects not seen before. Some examples of classification problems in bioinformatics are gene finding (sequence in, gene presence out), diagnostics (microarray data in, diagnosis out), data integration (measurements in, probability of interaction out), etc.

In this course, we will introduce basic techniques from the fields of pattern recognition and machine learning to solve such problems. We will introduce the pattern recognition pipeline: measuring, feature extraction and selection, classification and evaluation. The first two days will introduce the basic classification problem and a number of classic approaches to solve it. Next, methods for selecting or extracting informative features from a large set of measurements will be introduced. This will be followed by an introduction to a number of unsupervised techniques, that allow to find natural groupings or probabilistic descriptions of (unlabeled) data. The course will end with a cursory introduction to a number of intricate classifiers, artificial neural networks and support vector machines, and an overview of approaches to solve the generalization problem. For a large number of the methods discussed, we will turn to recent bioinformatics literature for examples.

## **Registration**

You can pre-register for this course by filling out the BioSB

enrolment form. The maximum number of participants is 25, so register soon to be sure of a course seat!

The course fee (to be determined) includes:

- Course material: handouts and a lab course manual will be handed out at the start of the course. Software required for the lab course (Matlab toolboxes) will be made available online.
- Catering: coffee, tea and lunch will be provided. Drinks will be organized in The Basket.

Information about hotel accommodation in Amsterdam can be found

here. Participants have to book (and pay for) the accommodation themselves if they need it. This is not included in the course fee.

## **Course material**

The course material is available

here (2013 version, will be updated for the 2015 course) and includes the handouts of the slides, a lab course manual and the required data and Matlab toolboxes. Note that there is no need to print out material; slide handouts and the lab course manual will be handed out in a folder to participants at the start of the course.

For the moment you are already advised to have a look at the following documents:

- To prepare for the course: a self-evaluation test (PDF, 90 Kb) on the prerequisite prior knowledge (probability theory and linear algebra). If you have a lot of trouble answering some of these exercises, consult the text books mentioned in the PDF, or a few primers (ZIP/PDF, 4.9 Mb) on these topics.
- The lab courses will make extensive use of Matlab. You do not need to be a fluent programmer, but if you have never worked with Matlab before it may help to try to get a hold of a copy of Matlab (your university may have a campus license) and have a look at the Appendices of the lab course manual. An extensive Matlab primer is also available. During the course Matlab and all software/data are available on the PCs in the lab, so there is no need to bring your laptop.

## **Examination**

Certificates of participation will be handed out at the end of the course. PhD students requiring a certificate of successful completion should perform additional work. The student will analyse a biological dataset (preferably one from his/her own practice) using the tools provided in the course, and write a small report (5-10 pages) on the results. If the student has no dataset available, one will be provided. The report will have to be mailed to

p.d.moerland@amc.uva.nl no later than three weeks after the course has finished. We will strictly adhere to this deadline; if you require extension, you should contact us well in advance. The proposal will be graded "fail" or "pass", with one possible resubmission.

## **Schedule**

The course will be given in Spring 2015. Preparation material on statistics and linear algebra will be distributed before the course, to be studied by students missing the required background. Finally, 2-3 days will have to be spent on the report to be handed in. Each day will have the following layout:

- 9.30 - 12.30 Lectures (F630, W&N building)
- 12.30 - 13.30 Lunch
- 13.30 - 17.30 Hands-on computer lab (S345, W&N building)

"W&N" means "Wis- en Natuurkundegebouw" (

map), Vrije Universiteit, De Boelelaan 1081a, Amsterdam. Travel directions can be found

here.

**Monday** -

**Introduction**
**Lecturer** Marcel Reinders

**Subjects** Introduction to pattern recognition: measurements, features, classification. Supervised vs. unsupervised learning, relation to regression. Bayesian framework: risk, cost; evaluation: ROCs, cross-validation. Density estimation: histograms, nearest neighbour, Parzen, Gaussian Bayesian classification.

**Tuesday** -

**Classifiers**
**Lecturer** Perry Moerland

**Subjects** Parametric classifiers: (D)LDA, (D)QDA. Nonparametric classifiers: k-NN, Parzen. Discriminant analysis: LDA, logistic regression. Decision trees and random forests.

**Wednesday** -

**Feature selection and extraction**
**Lecturer** Lodewyk Wessels

**Subjects** Feature selection: criteria, search algorithms (forward, backward, branch & bound). Sparse classifiers: Ridge, LASSO. Feature extraction: PCA, Fisher. Embeddings: MDS.

**Thursday** -

**Clustering and HMMs**
**Lecturer** Perry Moerland

**Subjects** Hierarchical clustering. Agglomerative clustering. Model-based clustering: mixtures-of-Gaussians, EM. Hidden Markov models.

**Friday** -

**Selected advanced topics**
**Lecturer** Marcel Reinders

**Subjects** Artificial neural networks. Support vector machines. Classifier ensembles. Complexity and regularisation.

For more information about the course programme, please contact

Perry Moerland; for more information about registration or logistics, please contact

Celia van Gelder.