Informatics 2B: Lecture Schedule

Semester 2

Lectures start in Week 1, i.e., the week starting 12 January 2014.

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).

Note and tutorial packs: please collect a complete set of notes and tutorial exercises from the ITO before the start of the course; the ITO will inform you by email once these are ready for collection. 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, 13 Jan, Hiroshi: Introduction to learning and data. (Chapter 1; slides-fullslides-multi; Matlab, Python)
  2. Thu, 15 Jan, KK: Introduction to Algorithms. (notesslides-fullslides-multi, Search.java. )
  3. Fri, 16 Jan, KK: Asymptotic Notation. (notesslides-fullslides-multi.)

  4. Tue, 20 Jan, Hiroshi: Similarity and recommender systems. (Chapter 2; slides-fullslides-multi)
  5. Thu, 22 Jan, KK: Asymptotic Notation and Algorithms. (no notes,  slides-fullslides-multi.)
  6. Fri, 23 Jan, KK: Sequential Data Structures. (notesslides-fullslides-multi.)

  7. Tue, 27 Jan, Hiroshi: Collaborative filtering, Clustering. (Chapter 3; slides-fullslides-multi)
  8. Thu, 29 Jan, KK: Hashing. (notesslides-fullslides-multi.)
  9. Fri, 30 Jan, Hiroshi: Classification and nearest neighbours. (Chapter 4; slides-fullslides-multi)

  10. Tue, 03 Feb, Hiroshi: Introduction to statistical pattern recognition. (Chapter 5; slides-fullslides-multi)
  11. Thu, 05 Feb, KK: AVL trees. (notesslides-fullslides-multi)
  12. Fri, 06 Feb, Hiroshi: Naive Bayes classification. (Chapter 6; slides-fullslides-multi)

  13. Tue, 10 Feb, Hiroshi: Text classification using Naive Bayes. (Chapter 7; slides-fullslides-multi)
  14. Thu, 12 Feb, KK: Priority Queues and Heaps (notesslides-fullslides-multi)
  15. Fri, 13 Feb, KK: MergeSort and Divide-and-Conquer (notesslides-fullslides-multi.)

    16-20 Feb, Innovative Learning Week, no lectures or tutorials

  16. Tue, 24 Feb, Hiroshi: Gaussians. (Chapter 8; slides-fullslides-multi)
  17. Thu, 26 Feb, KK: HeapSort and QuickSort. (notesslides-fullslides-multi.)
  18. Fri, 27 Feb, Hiroshi: Classification with Gaussians. (Chapter 9; slides-fullslides-multi)

  19. Tue, 04 Mar, Hiroshi: Discriminant functions. (Chapter 10; slides-fullslides-multi)
  20. Thu, 05 Mar, KK: Graphs I. (notesslides-fullslides-full.)
  21. Fri, 06 Mar, Hiroshi: Review: Gaussians and Linear discriminants. (slides-fullslides-multi)

  22. Tue, 10 Mar, Hiroshi: Single-layer neural networks 1. (Chapter 10 and Chapter 11; slides-fullslides-multi)
  23. Thu, 12 Mar, KK: Graphs II (notesslides-fullslides-full.)
  24. Fri, 13 Mar, Hiroshi: Single layer neural networks 2. (Chapter 11; slides-fullslides-multi)

  25. Tue, 17 Mar, Hiroshi: Multi-layer neural networks 1. (Chapter 12; slides-fullslides-multi)
  26. Thu, 19 Mar, KK: Large-scale Indexing and Sorting. (notesslides-fullslides-multi.)
  27. Fri, 20 Mar, Hiroshi: Multi-layer neural networks 2. (Chapter 12; slides-fullslides-multi)

  28. Tue, 24 Mar, Hiroshi: Review and conclusion of learning thread. (slides-fullslides-multi)
  29. Thu, 26 Mar, KK: Ranking Queries for the WWW. (notesslides-fullslides-multi.)
  30. Fri, 27 Mar, KK: Overview lecture, discussion and guide to revision.


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