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

• Lecture slides [PDF part 1, PDF part 2]
• Request DataCamp access: click here
• Recommended textbooks for self-study:
  • Machine Learning: A Probabilistic Perspective [Murphy 2012]
  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction [Hastie, Tibshirani, Friedman 2009]
  • Deep Learning [Courville, Goodfellow, Bengio 2015]
  • A First Course in Machine Learning [Rogers and Girolami 2016]
  • Information Theory, Inference, and Learning Algorithms [Mackay, 2005].
• Agenda:

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