Data Mining
Syllabus
- Introduction to machine learning
Week 1———–1.1 Data mining, big data analytics and data science.
Week 1———–1.2 Machine Learning.
Week 1———–1.3 Learning: supervised, unsupervised, by reinforcement - Supervised learning
Week 2———–2.1 Nearest neighbors.
Weeks 3-4——-2.2 Decision tree.
Weeks 5-6——-2.3 Bayesian classification.
Week 7———–2.4 Rule-Based classification. Rought sets.
Week 7———–2.5 Other classification techniques. SVM. - Unsupervised learning
Weeks 8-9——-3.1 Partitional clustering. Kmeans algorithm.
Week 10———3.2 Hierarchical clustering. Agglomerative algorithms. - Association techniques
Weeks 11-12—4.1 Apriori algorithm.
Week 13———4.2 Association rules generation algorithms. - Reinforcement learning
Week 14———5.1 Markov chain.
Week 14———5.2 Theoretical foundation of SARSA algorithm.
Weeks 15-16—5.3 SARSA algorithm.
Course Grading Policy The components of the course grade for each evaluation are:
- 60% Written exams (E1, E2, E3, E4, E5, E6, E7),
- 40% Laboratory exercises (L1, L2, L3, L4, L5, L6, L7).
- The passing grade for this course is 70 (evaluations average).
Course Administration
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Progress: it contains official syllabus, grades and attendance.
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Notes downloading: go to each topic of the English syllabus.
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Labs submission: see instructions.
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Exams schedule on Fridays:
E1-W2, E2-W4, E3-W6, E4-W9, E5-W12, E6-W14, E7-W1-12 -
Labs on Mondays: L1-W3; L2-W5; L3-W7; L4-W10; L5-W13; L6-W15, L7-W17.
Information Sources
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Han, J., Kamber, M. y Pei, J. (2011). Data Mining: Concepts and Techniques. Editorial Morgan Kaufman.
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Ian H y Frank Eibe (2005), Data mining: Practical Machine Learning Tools and Techniques Witten. Editorial Morgan Kaufmann.
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José Hernández Orallo, M.José Ramírez Quintana y Cèsar Ferri Ramírez (2004). Introducción a la Minería de Datos. Editorial Pearson.
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Bing Liu (1998). Web Data Mining. Editorial Springer.
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Dean J. (2014). Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners. Wiley and SAS Business Series.
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Richard S. Sutton y Andrew G. Barto (1998). Reinforcement Learning: An introduction. MIT Press.
- Kudyba, S. (2014). Big Data, Maining, and Analytics: Components of Strategic Decision Making. CRC Press.
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Hurwitz, J., Nugent, A., Halper, F., & Kaufman, M. (2014). Big Data for Dummies. John Willey & Sons.
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WEKA (download and datasets).
http://www.cs.waikato.ac.nz/ml/weka/index.html -
UCI: Machine learning repository
http://archive.ics.uci.edu/ml/ - Book list for machine learning.
http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/MLPAGES/mlbks.htm