Informatics 2B - Learning: Drop-in Labs

Note:

Demonstrator: Andreas Kapourani (Weeks 1-7)

  1. Week 01 (19 Jan.): Introduction to Matlab. Lab 1 Part 1 and Part 2
  2. Week 02 (26 Jan.): Similarity and recommender systems. Lab 2, and data.txt
  3. Week 03 (02 Feb.): K-means clustering and PCA. Lab 3, and data sets: faithful.txt, iris.txt
  4. Week 04 (09 Feb.): K-NN classification. Lab 4 and [lemon-orange.txt]
  5. Week 05 (16 Feb.): Statistical pattern recognition. Lab 5 and [fish.txt]
  6. Week 06 (02 Mar.): Naive Bayes and Introduction to Gaussians.
  7. Week 07 (09 Mar.): Classification with Gaussians.
  8. Week 08 (16 Mar.): Coursework 2
  9. Week 09 (23 Mar.): Coursework 2
  10. Week 10 (30 Mar.): Coursework 2
  11. Week 11 (TBC): Coursework 2


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