Data Science

Year 3 · Semester 2 · choose 1 of 2

• Being built now · Level 3 · 6 points

  Computer Vision and Pattern Recognition

  How a computer recognises what is in a picture: gradient histograms, local binary patterns, support vector machines, bag of visual words and Fisher's linear discriminant, all worked by hand on tiny images and tables.

  Prerequisites: no set prerequisites

  What you'll learn (10 topics)

  • Image filtering and edges
  • Gradient histograms and the HOG descriptor
  • Local binary patterns and histogram distances
  • Learning from data and evaluation
  • Support vector machines
  • Bayes, neighbour and tree classifiers
  • Features, PCA and clustering
  • Bag of visual words and image search
  • Fisher's linear discriminant
  • Convolutional networks

• Ready to learn · Level 3 · 6 points

  IT Research Methods and Thesis Skills

  How to do research in IT: forming a question, reviewing the literature, designing an experiment, analysing numbers and interviews, treating participants ethically and planning a thesis, all on invented studies.

  Prerequisites: no set prerequisites

  What you'll learn (5 topics)

  • Research questions and the literature review
  • Experimental design and threats to validity
  • Quantitative analysis
  • Qualitative methods
  • Ethics, privacy and thesis planning