1. Computational Methods for Data Analysis Massimo Poesio INTRODUCTION TO THE COURSE

2. Red: Attribute Blue: Tool Green: Location R+G=Yellow G+B=Cyan R+B=Pink R+G+B=White ML FOR DATA ANALYSIS IN THE COGNITIVE SCIENCES: SUPERVISED LEARNING AND CATEGORIES IN THE BRAIN

3. ML FOR DATA ANALYSIS IN THE COGNITIVE SCIENCES: DISTRIBUTIONAL SEMANTICS AND CLUSTERING

4. THIS COURSE An intro to Machine Learning for Cognitive Scientists Also: part of the Introduction to Artificial Intelligence course available to MSc students from CIMEC / Filosofia

5. APPROACH Part theory Part practical development – Using R (or MATLAB or OCTAVE if you prefer)

6. COURSE CONTENTS / TIMETABLE Week 1 – Today: Intro to ML, Crash intro to R – Tomorrow: Linear regression – Friday: Logistic regression Week 2 – Wed: Neural Nets – Thu: more Neural Nets – Fri: Support Vector Machines Week 3 – Unsupervised learning: Clustering, PCA – Practical tips on machine learning

7. READINGS Course slides from – http://clic.cimec.unitn.it/massimo/Teach/CMDA/ Machine Learning in R: – Conway & White, Machine Learning for Hackers, O’Reilly Machine Learning: – Mitchell, Machine Learning, Prentice-Hall R: – Baayen, Analyzing Linguistic Data, Cambridge

8. AI STUDENTS Come talk to me about the additional material

9. PRACTICAL INFORMATION 36 hours / 6 credits Evaluation – A PROJECT to be presented at the exam – Typical project: use supervised / unsupervised learning to analyze some data you have collected Web sites: http://clic.cimec.unitn.it/massimo/Teach/CMDA http://clic.cimec.unitn.it/massimo/Teach/AI