Informatics 2B: Lecture Schedule

Semester 2

Lectures start in Week 1, i.e., the week starting on 15 January 2018.

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, 16 Jan, Hiroshi: Introduction to learning and data. (Chapter 1; slides-fullslides-multi; Matlab, Python)
  2. Thu, 18 Jan, KK: Introduction to Algorithms. (notesslides-fullslides-multi, ADS thread related software, Search.java. )
  3. Fri, 19 Jan, KK: Asymptotic Notation. (notessupplement on inductionslides-fullslides-multi.)

  4. Tue, 23 Jan, Hiroshi: Similarity and recommender systems. (Chapter 2; slides-fullslides-multi, lecture recording)
  5. Thu, 25 Jan, KK: Asymptotic Notation and Algorithms. (no notes,  slides-fullslides-multi.)
  6. Fri, 26 Jan, KK: Sequential Data Structures. (notesslides-fullslides-multi.)

  7. Tue, 30 Jan, Hiroshi: Clustering and data visualisation. (Chapter 3; slides-fullslides-multi, lecture recording)
  8. Thu, 01 Feb, KK: Hashing. (notesslides-fullslides-multi.)
  9. Fri, 02 Feb, KK: MergeSort and Divide-and-Conquer (notesslides-fullslides-multi.)

  10. Tue, 06 Feb, Hiroshi: Classification and nearest neighbours. (Chapter 4; slides-fullslides-multi, lecture recording)
  11. Thu, 08 Feb, KK: AVL trees.ed (notesslides-fullslides-multi)
  12. Fri, 09 Feb, Hiroshi: Introduction to statistical pattern recognition and optimisation. (Chapter 5; slides-fullslides-multi, lecture recording)

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

    19-23 Feb, Flexible Learning Week, no lectures or tutorials

  16. Tue, 27 Feb, Hiroshi: Gaussians. (Chapter 8; slides-fullslides-multi, lecture recording)
  17. Thu, 01 Mar, KK: HeapSort and QuickSort. (notesslides-fullslides-multi.)
  18. Fri, 02 Feb, Hiroshi: Classification with Gaussians. (Chapter 9; slides-fullslides-multi, lecture recording)

  19. Tue, 06 Mar, Hiroshi: Discriminant functions. (Chapter 10; slides-fullslides-multi, lecture recording)
  20. Thu, 08 Mar, KK: Graphs I. (notesslides-fullslides-multi.)
  21. Fri, 09 Mar, Hiroshi: Single-layer neural networks 1. (slides-fullslides-multi, lecture recording)

  22. Tue, 13 Mar, Hiroshi: Single-layer neural networks 2. (Chapter 11; slides-fullslides-multi)
  23. Thu, 15 Mar, KK: Graphs II (notesslides-fullslides-full.)
  24. Fri, 16 Mar, Hiroshi: Single layer neural networks 3. (Chapter 11; slides-fullslides-multi, lecture recording)

  25. Tue, 20 Mar, Hiroshi: Multi-layer neural networks 1. (Chapter 12; slides-fullslides-multi, lecture recording)
  26. Thu, 22 Mar, KK: Large-scale Indexing and Sorting. (notesslides-fullslides-multi.)
  27. Fri, 23 Mar, Hiroshi: Multi-layer neural networks 2. (Chapter 12; slides-fullslides-multi, lecture recording)

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


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