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This is a repository for the tutorial materials in machine learning (COMP4211) in HKUST.

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COMP4211 (Spring2018)

This tutorial series is perpared by Kit Yeung

This is a repository to store the tutorials materials for COMP4211 - Mahcine Learning in HKUST.


To download any single file in this tutorial, you may follow this link.

Or you can clone this repository by:

git clone https://github.com/ckyeungac/COMP4211_Spring2018.git

Tutorial Schedule:

  1. Tutorial 1 (2018-02-09)
    • Introduction of the tutorial
    • Azura Machine Learning Studio
    • Introduction to Python
  2. Tutorial 2 (2018-02-23)
    • Introduction of machine learning work flow
    • Introduction to Numpy
    • Iris classification problem in scikit-learn
      • Iris dataset
      • Naive Bayes classifier
      • Train-test split
      • Accuracy
  3. Tutorial 3 (2018-03-02)
    • Sentiment Analysis in scikit-learn(Toy example)
      • Artificial neural network (ANN)
      • Text Vectorization
      • K-fold cross-validation (Only basic knowledge included, the detail implementation is left for your assignment 1)
    • Assignment 1
      • 20 newsgroups dataset
  4. Tutorial 4 (2018-03-09)
    • Perceptron (Implement from scratch)
    • ADALINE
    • Create a standalone workspace in Azure ML Studio.
  5. Tutorial 5, 6 (2018-03-16, 2018-03-23)
    • Basic TensorFlow
      • MNIST dataset
      • Single layer neural netowrk
  6. Tutorial 7 (2018-04-06)
    • Implement CNN using TensorFlow
  7. Tutorial 8 (2018-04-13)
    • SVM in scikit-learn
  8. Tutorial 9 (2018-04-20)
    • SVM in Azura Machine Learning Studio
      • Credit risk prediction
      • Different evaluation measures apart from accuracy
        • Confusion matrix
        • Precision, recall, F1-score
        • Area under the ROC curve (AUC)
  9. Tutorial 10 (2018-04-27)
    • Nonlinear SVM in scikit-learn
      • Kernel
        • Radial basis function (RBF)
        • Polynominal

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