AI@WWU — A Practical Introduction to AI Theory and Techniques for Interdisciplinary Research


IMPORTANT NOTES:

  • The course is already fully booked. However, please register on the waiting list so that we can estimate the demand and consider possible alternatives in case of high interest.
  • The start of this course has been postponed according to the guidelines of the university in order to slow the spread of COVID-19 (see official information here...).

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Short Summary:

AI and in particular machine learning (ML) tools become more and more accessible due to easy to use programming environments (esp. Python) and libraries (esp. Tensorflow and Pytorch). In order to apply these powerful tools for a variety of research projects, some basic understanding is required to tackle data preparation, visualisation and successful ML algorithm usage. In this course we will (1) teach AI and machine learning basics (70% of the course) and (2) apply these techniques to custom problems and custom data provided by the participants (30% of the course). In particular, we will introduce several state of the art deep learning algorithms like CNNs, LSTMs and Autoencoder. The entire course will be interactive and the participants will implement and use all presented techniques in pre-configured test environments.

  • Goal

    •  Teach AI and machine learning basics (70%)
    •  Work on own data and project (30%)

  • Targeted audience:


    University staff from all disciplines and career levels (doctoral students, PostDocs, Group Leaders,
    etc.)

    • Participants should be interested in learning the basic theory and practical usage of AI (in particular deep learning) algorithms
    • All disciplines are welcome (i.e. science and humanities with interest in quantitative methods)
    • Own datasets / problem sets are welcome; if these can be used needs to be evaluated in the
      beginning of this course
  • Requirements

    •  Programming skills are not required; some basic experience is however desirable (preferably Python; programming language introduction including basics will be given in the course)
    • Basic knowledge in statistics and linear algebra

  • Syllabus

    1. Python basics
      • Programming language and environment (Jupyter Notebooks)
      • Importing, exporting and visualising data
      • Fundamental libraries (Numpy, Scikit-learn, Matplotlib)
    2. Machine Learning basics
      • Brief conceptual overview of different techniques (supervised, unsupervised etc.)
      • Introduction to classical machine learning techniques (SVM, Decision Trees, Clustering, …)
      • Perceptron and neural networks (loss, activation function, etc.)
      • Brief intro to Gradient Decent (Backpropagation)
      • Practical Example 1: Neural networks for regression (1D and 2D functions, under- and overfitting, etc.)
      • Practical Example 2: Neural networks for character recognition (MNIST)
    3. State-of-the-art (deep) Machine Learning concepts
      • Convolutional neural networks (CNNs)
        • Practical Example 3: CNNs for character recognition (MNIST)
        • Practical Example 4: CNNs for object classification (CIFAR-10)
      • Recurrent Neural Networks (especially LSTMs)
        • Practical Example 5: LSTMs for sequence learning
      • Autoencoder
        • Practical Example 6: Autoencoder for dimensionality reduction
    4. Advanced training concepts
      • Regularisation techniques (dropout, batch normalisation, data augmentation)
      • Transfer learning
    5. AI @ own dataset
      • Final Project: ML on own dataset

  • Literature

    • Practical Literature: “Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow” (2nd edition) by Aurelien Geron (primary resource for this course!)
    • Theoretical Literature (not required for this course):
      • “Deep Learning” (1st edition) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
      • “Pattern Recognition and Machine Learning” (1st edition (corrected)) by Christopher Bishop
      • “Artificial Intelligence: A Modern Approach” (3rd edition) by Stuart Russel and Peter Norvig
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