Informatics 2B: Lecture Schedule

Semester 2

Notes and slides are linked from this page. Slides are given both as "full" (one slide per page, easier to view on a screen) and as "multi" (typically 4 or 6 per page, saves on paper if printed).

Links will normally be made a day before the relevant lecture. Do not report broken links before this time.

Old lecture videos available online at http://groups.inf.ed.ac.uk/vision/VIDEO/2008/inf2b.htm

Note and tutorial packs: please collect a complete set of notes and tutorial exercises from the ITO. You will need to present your matriculation card for this (and so will not be able to collect on behalf of others). Each student is entitled to one pack free of charge.

  1. Tue 17 Jan, Guido: Introduction to learning and data. (Chapter 1, part of chapter 2; slides-full,  slides-multi)
  2. Thu 19 Jan, KK: Introduction to Algorithms. (notes,  slides-full,  slides-multi, Search.java. )
  3. Fri 20 Jan, KK: Asymptotic Notation. (notes,  slides-full,  slides-multi.)
  4. Tue 24 Jan, Guido: Similarity and recommender systems. (Chapter 2; slides-full,  slides-multi)
  5. Thu 26 Jan, KK: Asymptotic Notation and Algorithms. (no notes,  slides-full,  slides-multi.)
  6. Fri 27 Jan, KK: Sequential Data Structures. (notes,  slides-full,  slides-multi.)
  7. Tue 31 Jan, Guido: Clustering. (Chapter 3; slides-full,  slides-multi)
  8. Thu 02 Feb, KK: Hashing. (notes,  slides-full,  slides-multi.)
  9. Fri 03 Feb, Guido: Classification and nearest neighbours. (Chapter 4; slides-full,  slides-multi)
  10. Tue 07 Feb: Guido: Introduction to statistical pattern recognition. (Chapter 5; slides-full,  slides-multi)
  11. Thu 09 Feb, KK: AVL trees. (notes,  slides-full,  slides-multi)
  12. Fri 10 Feb, Guido: Naive Bayes classification. (Chapter 6; slides-full,  slides-multi)
  13. Tue 14 Feb, Carol MacDonald: Careers Presentation.
  14. Thu 16 Feb, KK: Priority Queues and Heaps (notes,  slides-full,  slides-multi)
  15. Fri 17 Feb, KK: MergeSort and Divide-and-Conquer (notes,  slides-full,  slides-multi.)

    20-25 Feb, Innovative Learning Week, no lectures or tutorials

  16. Tue 28 Feb, Guido: Text classification using Naive Bayes. (Chapter 7; slides-full,  slides-multi)
  17. Thu 01 Mar, KK: HeapSort and QuickSort. (notes,  slides-full,  slides-multi.)
  18. Fri 02 Mar, Guido: Gaussians. (Chapter 8; slides-full,  slides-multi)
  19. Tue 06 Mar, Guido: Classification with Gaussians. (Chapter 9; slides-full,  slides-multi)
  20. Thu 08 Mar, KK: Graphs I. (notes,  slides-full.)
  21. Fri 09 Mar, Guido: Discriminant functions. (Chapter 10; slides-full,  slides-multi)
  22. Tue 13 Mar, Guido: Single-layer neural networks 1. (Chapter 10--11; slides-full,  slides-multi)
  23. Thu 15 Mar, KK: Graphs II (notes,  slides-full.)
  24. Fri 16 Mar, Guido: Single layer neural networks 2. (Chapter 11; slides-full,  slides-multi)
  25. Tue 20 Mar, Guido: Multi-layer neural networks 1. (Chapter 12; slides-full,  slides-multi)
  26. Thu 22 Mar, KK: Large-scale Indexing and Sorting. (notes,  slides-full,  slides-multi.)
  27. Fri 23 Mar, Guido: Multi-layer neural networks 2 and Generalisation. (Chapter 12; slides-full,  slides-multi)
  28. Tue 27 Mar, Guido: Review and conclusion of learning thread.
  29. Thu 29 Mar, KK: Ranking Queries for the WWW. (notes,  slides-full,  slides-multi.)
  30. Fri 30 Mar, KK: Overview lecture, discussion and guide to revision.


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