Data Mining


  1. 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
  2. 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.
  3. Unsupervised learning
    Weeks 8-9——-3.1 Partitional clustering. Kmeans algorithm.
    Week 10———3.2 Hierarchical clustering. Agglomerative algorithms.
  4. Association techniques
    Weeks 11-12—4.1 Apriori algorithm.
    Week 13———4.2 Association rules generation algorithms.
  5. 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:

Course Administration

  1. Progress: it contains official syllabus, grades and attendance.

  2. Notes downloading: go to each topic of the English syllabus.

  3. Labs downloading: Lab1, Lab2, Lab3, Lab4, Lab5, Lab6, Lab7

  4. Labs submission: see instructions.

  5. Exams schedule on Fridays:
    E1-W2, E2-W4, E3-W6, E4-W9, E5-W12, E6-W14, E7-W1-12

  6. Labs on Mondays: L1-W3; L2-W5; L3-W7; L4-W10; L5-W13; L6-W15, L7-W17.

Information Sources

  1. Han, J., Kamber, M. y Pei, J. (2011). Data Mining: Concepts and Techniques. Editorial Morgan Kaufman. 

  2. Ian H y Frank Eibe (2005), Data mining: Practical Machine Learning Tools and Techniques Witten. Editorial Morgan Kaufmann. 

  3. José Hernández Orallo, M.José Ramírez Quintana y Cèsar Ferri Ramírez (2004). Introducción a la Minería de Datos. Editorial Pearson. 

  4. Bing Liu (1998). Web Data Mining. Editorial Springer. 

  5. Dean J. (2014). Big Data, Data Mining, and Machine Learning: Value Creation for Business 
Leaders and Practitioners. Wiley and SAS Business Series. 

  6. Richard S. Sutton y Andrew G. Barto (1998). Reinforcement Learning: An 
introduction. MIT Press. 

  7. Kudyba, S. (2014). Big Data, Maining, and Analytics: Components of Strategic 
Decision Making. CRC Press. 

  8. Hurwitz, J., Nugent, A., Halper, F., & Kaufman, M. (2014). Big Data for Dummies. John Willey & Sons.

  9. WEKA (download and datasets).

  10. UCI: Machine learning repository

  11. Book list for machine learning.