QBI 2019

Machine and deep learning in bioimage analysis and microscopy: Tutorial introduction for microscopists [conference link]

11:00 – 12:30 What is machine learning? (Seth Flaxman, Imperial College)

  • Supervised vs unsupervised learning: k-nearest neighbors, k-means clustering
  • From linear to nonlinear methods: transformations, feature engineering, and the kernel trick
  • Bias vs variance, and how to deal with the problem of overfitting.
  • Regularization, lasso, ridge regression, and the elasticnet

12:30 – 13:30 Lunch

13:30 – 14:30 Advanced machine learning (Seth Flaxman, Imperial College)

  • Ensemble methods
  • Gaussian processes

14:30 – 15:45 Optimization (Julien Mairal, INRIA)

In this lecture, we will discuss a few techniques recently introduced in machine learning and optimization to deal with large amounts of data. We will focus on regularized empirical risk minimization problems, which consists of minimizing a large sum of functions, and cover also stochastic optimization techniques for minimizing expectations. Concepts we are planning to cover include

  • Stochastic gradient descent techniques with variance reduction
  • Nesterov’s acceleration
  • Quasi-Newton techniques.

We will also consider variants that allow dealing with nonsmooth regularization such as the l1-norm, which is useful for sparse estimation in high dimension.

15:45 – 16:45 Deep Learning (Christophe Zimmer, Institut Pasteur)

  • what deep learning can do
    • image classification
    • other applications
  • how deep learning works
    • forward propagation
    • convnets
    • training
    • babysitting neural nets

DIY deep learning with Keras

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