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Mart van de Ven edited this page Feb 11, 2014 · 16 revisions

Lab Materials

Introductions

Regression Algorithms

Classification Algorithms

  • Lesson 07 : KNN Classification
  • Lesson 08 : Probability, Naive Bayes, AUC
  • Lesson 09 : Decision Trees, Confusion Matrices, Precision/Recall
  • Lesson 10 : Support Vector Machines

Unsupervised Learning and Advanced Techniques

  • Lesson 11 : K-Means Clustering
  • Lesson 12 : (Kernel) Principal Component Analysis and Singular Value Decomposition
  • Lesson 13 : Ensemble Methods, Adaboost, and Random Forests
  • Lesson 15 : Time Series Analytics and Prediction
  • Lesson 16 : Distributed Systems, MapReduce and Hadoop

Guest Speakers and Project Development

  • TBC

Assignments

Ongoing Assignments

  • Regressions: Prediction Baseball Salaries
  • Classification: Predicting Bad Used Car Purchases

Final Projects

Guides

Appendix

  • Reading Journals and Writing Journal Reviews
  • Metrics for Regressions
  • Next Steps
  • Recommended Reading

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