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
Person in charge
Javier Béjar Alonso (
Miquel Sanchez Marre (
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
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.
CEP1 - Capability to solve the analysis of information needs from different organizations, identifying the uncertainty and variability sources.
Generic Technical Competences
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.
To known and use advanced unsupervised machine learning and data mining techniques for application on all the domains of engineering and science
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
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)
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, ...)
Semi supervised clustering
This topic will include current semi supervised algorithms for clustering data (based on constraints, based on rules, markov random fields)
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, ..)
This topic will include and introduction to association rules algorithms and their relationship with unsupervised learning algorithms and clustering
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
Fundamentals of Case-Based Reasoning
Cognitive theories. Basic cycle of CBR reasoning. Academic Demosntrators.
CBR System Components
Case Structure. Case Library Structure. Retrieval. Adaptation/Reuse. Evaluation/Repair. Learning/Retain
A complex real-world example. OPENCASE/GESCONDA-CBR: a domain-independent CBR System .
CBR Development Problems
Competence. Space Efficiency. Time Efficiency.
Reflective/Introspective Reasoning in CBR
Introspection reasoning. Case Base maintenance.