Advanced Machine Learning Techniques

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Credits
5
Department
CS
Types
Elective
Requirements
This subject has not requirements
This course will introduce to different advanced tecniques in machine learning, data mining and case based reasoning. The machine learning part is oriented to unsupervised learning algorithms for structured (sequences, streams, graphs) and unstructured data. The case based reasoning part os oriented to the design and developements of this systems
Web: http://www.lsi.upc.edu/~bejar/amlt/amlt.html

Teachers

Person in charge

  • Javier Béjar Alonso ( )

Others

  • Miquel Sanchez Marre ( )

Weekly hours

Theory
3
Problems
0
Laboratory
0
Guided learning
0.21
Autonomous learning
5.7

Competences

Transversal Competences

Basic

  • CB7 - Ability to integrate knowledges and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.

Sustainability and social compromise

  • CT2 - Capability to know and understand the complexity of economic and social typical phenomena of the welfare society; capability to relate welfare with globalization and sustainability; capability to use technique, technology, economics and sustainability in a balanced and compatible way.

Solvent use of the information resources

  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

Technical Competences of each Specialization

Academic

  • CEA12 - Capability to understand the advanced techniques of Knowledge Engineering, Machine Learning and Decision Support Systems, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
  • CEA13 - Capability to understand advanced techniques of Modeling , Reasoning and Problem Solving, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.

Professional

  • CEP1 - Capability to solve the analysis of information needs from different organizations, identifying the uncertainty and variability sources.

Generic Technical Competences

Generic

  • CG1 - Capability to plan, design and implement products, processes, services and facilities in all areas of Artificial Intelligence.
  • CG3 - Capacity for modeling, calculation, simulation, development and implementation in technology and company engineering centers, particularly in research, development and innovation in all areas related to Artificial Intelligence.

Objectives

  1. To known and use advanced unsupervised machine learning and data mining techniques for application on all the domains of engineering and science
    Related competences: CB7, CT2, CT4, CEA12, CEA13, CEP1, CG1, CG3,

Contents

  1. Data Mining, a global perspective
    Brief introduction to what is Data Mining and Knowledge Discovery, the areas they are related to and the different techniques involved
  2. Unsupervised data preprocessing/transformation
    This topic will include different algorithms for unsupervised data preprocessing such as data normalization, discretization, dimensionality reduction and feature extraction (PCA, ICA, SVD, linear and non linear, multidimensional scalling and non negative matrix factorization)
  3. Unsupervised Machine Learning/Numerical Taxonomy
    This topic will include classical and current algorithms for unsupervised learning from machine learning and statistics including hierarchical and parititional algorithms (K-means,Fuzzy C-means, Gaussian EM, graph partitioning, density based algorithms, grid based algorithms, unsupervised ANN, affinity propagation, ...)
  4. Semi supervised clustering
    This topic will include current semi supervised algorithms for clustering data (based on constraints, based on rules, markov random fields)
  5. Unsupervised methodologies in Knowledge Discovery and Data Mining
    This topic will include current trends on knowledge discovery for data mining and big data, (scalability, any time clustering, one pass algorithms, approximation algorithms, distributed clustering, ..)
  6. Association Rules
    This topic will include and introduction to association rules algorithms and their relationship with unsupervised learning algorithms and clustering
  7. Mining sequential and structured data
    This topic will include algorithms for unsupervised learning with sequential data and structured data, such as mining frequent sequences, strings, time series clustering and frequent motifs, clustering data streams, clustering graphs and social networks and discovering frequent subgraphs
  8. Fundamentals of Case-Based Reasoning
    Cognitive theories. Basic cycle of CBR reasoning. Academic Demosntrators.
  9. CBR System Components
    Case Structure. Case Library Structure. Retrieval. Adaptation/Reuse. Evaluation/Repair. Learning/Retain
  10. CBR Application
    A complex real-world example. OPENCASE/GESCONDA-CBR: a domain-independent CBR System .
  11. CBR Development Problems
    Competence. Space Efficiency. Time Efficiency.
  12. Reflective/Introspective Reasoning in CBR
    Introspection reasoning. Case Base maintenance.
  13. CBR Applications and CBR Software Tools
    Industrial applications. Software tools. Recommender systems.
  14. CBR System Evaluation
    Technical criteria. Ergonomic criteria.
  15. Advanced Research Issues in CBR
    Temporal CBR. Spatial CBR. Hybrid CBR systems.

Teaching methodology

Presentation classes and group project classes

Evaluation methodology


Work on the state of the art for a particular topic on the first part of the course.

Implementation of a case-based reasoning system for the second part of the course