Informatics 2B: Lecture Schedule

Semester 2

Lectures start in Week 1, i.e., the week starting on 16 January 2017.

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; you will be informed 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, 17 Jan, Hiroshi: Introduction to learning and data. (Chapter 1; slides-fullslides-multi; Matlab, Python)
  2. Thu, 19 Jan, KK: Introduction to Algorithms. (notesslides-fullslides-multi, ADS thread related software, Search.java. )
  3. Fri, 20 Jan, KK: Asymptotic Notation. (notessupplement on inductionslides-fullslides-multi.)

  4. Tue, 24 Jan, Hiroshi: Similarity and recommender systems. (Chapter 2; slides-fullslides-multi, lecture recording)
  5. Thu, 26 Jan, KK: Asymptotic Notation and Algorithms. (no notes,  slides-fullslides-multi.)
  6. Fri, 27 Jan, KK: Sequential Data Structures. (notesslides-fullslides-multi.)

  7. Tue, 31 Jan, Hiroshi: Clustering and data visualisation. (Chapter 3; slides-fullslides-multi, lecture recording)
  8. Thu, 02 Feb, KK: Hashing. (notesslides-fullslides-multi.)
  9. Fri, 03 Feb, KK: MergeSort and Divide-and-Conquer (notesslides-fullslides-multi.)

  10. Tue, 07 Feb, Hiroshi: Classification and nearest neighbours. (Chapter 4; slides-fullslides-multi, lecture recording)
  11. Thu, 09 Feb, KK: AVL trees.ed (notesslides-fullslides-multi)
  12. Fri, 10 Feb, Hiroshi: Introduction to statistical pattern recognition and optimisation. (Chapter 5; slides-fullslides-multi, lecture recording)

  13. Tue, 14 Feb, Hiroshi:Naive Bayes classification. (Chapter 6; slides-fullslides-multi, lecture recording)
  14. Thu, 16 Feb, KK: Priority Queues and Heaps (notesslides-fullslides-multi)
  15. Fri, 17 Feb, Hiroshi: Text classification using Naive Bayes. (Chapter 7; slides-fullslides-multi)

    20-24 Feb, Flexible Learning Week, no lectures or tutorials

  16. Tue, 28 Feb, Hiroshi: Gaussians. (Chapter 8; slides-fullslides-multi)
  17. Thu, 02 Mar, KK: HeapSort and QuickSort. (notesslides-fullslides-multi.)
  18. Fri, 03 Feb, Hiroshi: Classification with Gaussians. (Chapter 9; slides-fullslides-multi)

  19. Tue, 07 Mar, Hiroshi: Discriminant functions. (Chapter 10; slides-fullslides-multi)
  20. Thu, 09 Mar, KK: Graphs I. (notesslides-fullslides-full.)
  21. Fri, 10 Mar, Hiroshi: Single-layer neural networks 1. (slides-fullslides-multi)

  22. Tue, 14 Mar, Hiroshi: Single-layer neural networks 2. (Chapter 11; slides-fullslides-multi)
  23. Thu, 16 Mar, KK: Graphs II (notesslides-fullslides-full.)
  24. Fri, 17 Mar, Hiroshi: Single layer neural networks 3. (Chapter 11; slides-fullslides-multi)

  25. Tue, 21 Mar, Hiroshi: Multi-layer neural networks 1. (Chapter 12; slides-fullslides-multi)
  26. Thu, 23 Mar, KK: Large-scale Indexing and Sorting. (notesslides-fullslides-multi.)
  27. Fri, 24 Mar, Hiroshi: Multi-layer neural networks 2. (Chapter 12; slides-fullslides-multi)

  28. Tue, 28 Mar, Hiroshi: Review and conclusion of learning thread. (slides-fullslides-multi)
  29. Thu, 30 Mar, KK: Ranking Queries for the WWW. (notesslides-fullslides-multi.)
  30. Fri, 31 Mar, KK: Overview lecture, discussion and guide to revision.


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