Introduction to Machine Learning

DATES: 27 November - 1 December 2017 (5 sessions)
TIME: 09:00 - 11:00 (a total of 10 hours)

This course is an introduction to Machine Learning. Toy and real examples will be used to illustrate the steps for solving a ML problem. We will also illustrate new trends on ML through complex real world problems.
In order to give the insights of ML, this course aims to be a hands-on approach to the most widely used ML techniques and algorithms. We will use Jupyter Notebooks in Python to illustrate the different methods.

OBJECTIVES
- Get familiar with the different ML methods and applications.
- Understand the general workflow behind a ML application.
- Get the flavour of standard free software in the ML community.
- Discuss new trends and topics related to ML in industry and academia.

PROGRAMME
1. Introduction
2. Data pre-processing
a. Data cleaning
b. Feature extraction and selection
3. Data processing
a. Clustering
b. Classification
c. Regression
4. Model selection and validation
5. Discussion and new trends

PREREQUISITES
There are no formal prerequisites, but some basic knowledge of Statistics is expected. Master and PhD students are encouraged to participate. The code used in class will be made available and the students are encouraged to bring their laptop.

*Registration is free, but inscription is required before 22nd November: So as to inscribe send an e-mail to roldan@bcamath.org. Student grants are available. Please, let us know if you need support for travel and accommodation expenses.

• BCAM - UPV/EHU Course CERNUDA, DOMÍNGUEZ, IRUROZKI & PÉREZ