Informatics 2B: Lecture Schedule

Semester 2

Lectures start in Week 1, i.e., the week starting on 13 January 2020.

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.

Lecture recordings: Lectures are recorded automatically. The recordings can be accessed by logging in to Learn. Currently it is not possible to supply direct links from this page.

  1. Tue, 14 Jan: Introduction to learning and data. (Chapter 1; slides-fullslides-multi; Matlab, Python)

  2. Tue, 21 Jan: Similarity and recommender systems. (Chapter 2; slides-fullslides-multi)

  3. Tue, 28 Jan: Clustering and data visualisation. (Chapter 3; slides-fullslides-multi)

  4. Tue, 04 Feb: Classification and nearest neighbours. (Chapter 4; slides-fullslides-multi)
  5. Fri, 07 Feb: Introduction to statistical pattern recognition and optimisation. (Chapter 5; slides-fullslides-multi)

  6. Tue, 11 Feb: Naive Bayes classification. (Chapter 6; slides-fullslides-multi)
  7. Fri, 14 Feb: Text classification using Naive Bayes. (Chapter 7; slides-fullslides-multi)

    17-21 Feb, Flexible Learning Week, no lectures or tutorials

  8. Tue, 25 Feb: Gaussians. (Chapter 8; slides-fullslides-multi)
  9. Fri, 28 Feb: Classification with Gaussians. (Chapter 9; slides-fullslides-multi)

  10. Tue, 03 Mar: Discriminant functions. (Chapter 10; slides-fullslides-multi)
  11. Fri, 06 Mar: Single-layer neural networks 1. (slides-fullslides-multi)

  12. Tue, 10 Mar: Single-layer neural networks 2. (Chapter 11; slides-fullslides-multi)
  13. Fri, 13 Mar: Single layer neural networks 3. (Chapter 11; slides-fullslides-multi)

  14. Tue, 17 Mar:Multi-layer neural networks 1. (Chapter 12; slides-fullslides-multi)
  15. Fri, 20 Mar: Multi-layer neural networks 2. (Chapter 12; slides-fullslides-multi)
  16. Tue, 24 Mar: Review and conclusion of learning thread. (slides-fullslides-multi)


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