DISCnet Machine Learning Course

Notes, demos and materials for learning Machine Learning

Extra materialS

In addition to the material in this git repository, I've also used materials from my computer vision and data mining modules. Please feel free to take a look at the lecture slides and notes for these which can be found here:

• http://comp3204.ecs.soton.ac.uk
• http://comp6237.ecs.soton.ac.uk

Rough Plan

(Note that this is only a guide. We'll adapt the content to your needs during the course.)

• Tuesday: Introduction to Machine Learning
  • Leaders: Prof Niranjan and Dr Hare
  • Topics Covered:
    • The perceptron/Bayes optimal decisions
    • Feature selection and Lasso
    • MLPs
    • Gradient learning, SGD, momentum
    • Evaluating performance
      • ROC curves
    • Making sense of data intro (Text and Bags of Words)
    • Machine Learning 101 - classifying text
• Wednesday: Advanced Machine Learning
  • Leader: Prof Adam Prugel-Bennett
  • Topics Covered:
    • Generalisation
      • Bias-Variance Dilema
    • Ensemble Techniques
      • Ada-boost, random forest
    • Kernel methods
      • SVM
      • kernels
    • Probabilistic techniques
      • Gaussian Processes
    • Making sense of data
      • Types of data (images, text, numbers)
        • Encoding data and feature extraction
        • Data preparation, missing data
          • Balancing data
• Thursday: Deep Learning
  • Leader: Dr Jonathon Hare
  • Topics Covered:
    • Why Deep
      • CNNs
      • RNNs (LSTM, etc.)
    • Word Embeddings
    • Loss functions
    • GPU programming (libraries)
    • Keras tutorial 1 - building simple CNNs
    • Transfer Learning
    • Keras tutorial 2 - transfer learning with CNNs
    • Keras tutorial 3 - Text classification
    • Keras tutorial 4 - Sequence modelling