Outline

1. Introduction to Big Data and Machine Learning (ML)

2. Dimension Reduction Fundamentals

3. Dimension Reduction Methods in Big Data

4. Classification in Machine Learning

5. Introduction to image analysis

6. Specific Applications in Image Processing:

   • Image segmentation using ML

   • Semantic segmentation

   • Image classification 

Resources

Course information

Full material

• Lecture 1

  • Slides

  • Practical 1

  • Python notebooks

    • MNIST visualization

    • 1D, 2D, and 3D visualization

    • Image segmentation using clustering

    • Image classification using SVM

• Other resources

  • Paper: Joint exploration of kernel functions potential

  • Lecture notes on dimensionality reduction (PCA and Kernel PCA) 

  • Lecture notes on data classification (SVM)

  • Slides - Semantic segmentation of images