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Artificial Intelligence: Machine and statistical learning - 2025

 

Artificial intelligence: Machine and Statistical Learning

Course Leader

Herbert Kruitbosch

Target Group

This course is intended for NSIs staff from any field who want to learn more about Machine and Statistical Learning (MSL) method and techniques. MSL is concerned with algorithms that automatically improve their performance through 'learning', it has emerged mainly from statistics and artificial intelligence, and has connections to a variety of related subjects including computer science and pattern recognition. This course will present an overview of fundamental concepts, common techniques, and algorithms in MSL. It will cover basic topics such as dimensionality reduction, classification and regression, clustering as well as more recent topics such as ensemble learning/boosting, support vector machines, and kernel methods, etc... This course will provide students the basic intuition behind modern MSL methods.

 

Entry Qualifications
  • Sound command of English. Participants should be able to make short interventions and to actively participate in discussions. 

  • Programming in Python

  • Some experience looking at tabular data with scatter plots and histograms

 

Objective(s)
  • Validation and reporting results of machine learning methods.

  • Mathematical concepts of supervised and unsupervised machine and deep learning models, like PCA, SVM, trees, ensembles and neural networks.

  • Use of scikit-learn, matplotlib, pandas, tensorflow, keras to design models and perform machine learning experiments

  • Understanding of symbolic computation for backpropagation and gradient descent

  • Model selection, Hyper-parameter tuning and practical considerations

  • Understanding the lego bricks of neural networks (deep learning) and the numerical issues, in particular vanishing gradients.

  • Use pretrained models for text and vision applications with libraries like deeppavlov and detectron2.

 

Contents

The sessions should consist of both basic methodology and practical exercises.

  • Fundamental concepts of MSL: supervised, unsupervised, reinforcement and ensemble learning; maximum likelihood; loss functions; train, test and validation sets/errors; bias-variance trade-off; model complexity, regularisation and overfitting; etc...

  • Dimensionality reduction, e.g., principal component analysis, linear discriminant analysis.

  • Validation and resampling, e.g., cross-validation, bootstrap, jackknife.

  • Regression and variable selection, e.g., linear regression, model selection, ridge and lasso.

  • Classification and clustering, e.g., Bayes classifiers, K Nearest Neighbours, logistic regression, K-Means, hierarchical clustering, mixture models.

  • Decision, e.g., classification and regression trees, bagging, Random Forests, bayesian networks.

  • Pattern recognition and anomaly detection, e.g., kernel methods and support vector machines.

  • Introduction to artificial intelligence, neural networks and deep learning.

  • Practice using R or Python language and packages.

  •  

Expected Outcome

Practical skills to use scikit-learn and similar libraries to answer (research) questions with machine learning

Understanding of different machine learning models and neural networks

Overview and coarse skills w.r.t. neural networks text mining and computer vision

 

Training Methods
  • Lectures

  • Programming exercises

 

Required Reading 

None

Suggested Reading

Suggestions for when you like a structured overview of Machine or deep learning. They are not required at all.

  • Python Machine Learning - Effective algorithms for practical machine learning and deep learning, Sebastian Raschka, Vahid Mirjalili

  • Deep Learning with Keras, Antonio Gullì, Sujit Pal (practical)

  • Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville (theoretical)

Interesting papers which improved the field of deep learning significantly:

  • Understanding the difficulty of training deep feedforward neural networks, Xavier Glorot, Yoshua Bengio

  • Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate, Shift Sergey Ioffe, Christian Szegedy

  • Deep Residual Learning for Image Recognition, Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun

 

Required Preparation

Participants have to send two exercises a few weeks in advance to avoid issues with logging in to Google Colaboratory and browser problems at the course.

 

Trainer(s)/
Lecturer(s)

Herbert Kruitbosch (University of Groningen)

Marco Puts (Statistics Netherlands)

 

 

Practical Information

When

Duration

Where

Organiser

Application via National Contact Point

17-19.03.2025

3 days

The Hague, Netherlands

ICON-INSTITUT Public Sector GmbH

Deadline: 20.01.2025