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42030 - Advanced Techniques in Machine Learning (TAVA) [UPC]


Type: S3 Course
Semester: Fall
ECTS: 6
Teaching Points: 15
Offer: Annual
Responsible Unit: CS-UPC
Responsible: Javier Béjar
Language: English
Requirements:

GOALS

This course is an extension of the methodologies learned from the previous machine learning courses. The main subjects treated in this couse include unsupervised learning from diferent perspectives (machine learning, intelligent data analysis, knowledge discovery) and case based reasoning.


CONTENTS

Part I – Unsupervised Learning

1. Unsupervised Learning and Data Mining
2. Elements of the process of unsupervised learning
  • Data representation
  • Data preprocessing and transformation
  • Distance functions
3. Unsupervised learning ouside machine learning
  • Cognitive Psicology
  • Numerical Taxonomy
  • Data analysis
4. Machine learning perspective
  • Conceptual Clustering
  • Concept formation
  • Evaluation of unsupervised learning
  • Sintatic Biasing, Semantic Biasing
5. Unsupervised Learning in Knowledge Discovery/Data Mining
  • Clustering Algorithms for Knoledge Discovery
  • Unsupervised Learning from patterns and structures
  • Association Rules, Time Series, Trees, Graphs

Part II – Case Based Learning and Reasoning

1. Introduction
2. CBR Foundations
  • Basic cicle of reasoning
  • Models for expertise representation
3. CBR Applications
  • Academic application: CHEF, CASEY, JULIA, HYPO, PROTOS
  • Practical application to complex domains
  • OPENCASE: A domain independent CBR system
4. Cases representation and organization
  • Representation structures
  • Library structures
5. CBR system phases
  • Case retrieval
  • Similarity evaluation
  • Adaptation strategies and/or methods
  • Learning
6. Reflective reasoning in CBR
7. Applications and tools for CBR developement
  • Industrial applicacions
  • Software tools
8. CBR evaluation
9. Advanced Research in CBR