Notes, demos and materials for learning Machine Learning

(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