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Master in AI

In this repository, I present some of my works related with Artificial Intelligence at University of Lübeck, during my Master in Robotics and Autonomous Systems. I would like to thank all of my teachers and lab teammates.

Courses: Machine Learning, Computer Vision, Artificial Intelligence, Medical Deep Learning

Machine Learning

  1. PCA, ICA and Sparse Coding

    Three unsupervised methods are applied: Prinicipal Component Analysis (PCA), Independent Component Analysis (ICA), and Sparse Coding (SC).

    Dataset: MNIST - handwritten images and faces.

  2. Manifold Learning

    Multi-dimensional Scaling and Isomap algorithm are implemented. Dataset: Swiss roll.

  3. Statistical Learning Theory

    K-nearest-neighbor algorithm is implemented for a two-class classification problem on a two-dimensional dataset.

  4. Boosting

    Adaptive boosting algorithm is implemented to combine multiple weak classification models and form one strong classifier for a two-class classification problem on a two-dimensional dataset.

  5. Random Forests

    Random forest algorithm is implemented to combine multiple decision trees to one strong classifier for a multi-class classification problem on a two-dimensional dataset.

Computer Vision

  1. Introduction to Python

    • Basic Python: Basic data types (Containers, Lists, Dictionaries, Sets, Tuples), Functions, Classes
    • Numpy: Arrays, Array indexing, Datatypes, Array math, Broadcasting
    • Matplotlib: Plotting, Subplots, Images
    • IPython: Creating notebooks, Typical workflows
  2. Imaging - Histograms

    • Image gradients
    • Compute and display histograms
    • Underexposed, darker, images that have been exposed to too little light
    • Overexposed, brighter, images that have been exposed to too much light
    • Histogram equalization, logarithmic and quadratic functions
  3. Image Acquisition, Optics

    • Thin lenses, focal length, focus
    • The influence of the focal length
    • The influence of the aperture
    • Conversion between pixel and camera coordinates, Time-of-flight (ToF) camera
  4. Image Center, Edges, Keypoints

    • Finding the image center
    • Edge detection, show image gradients
    • Canny edge detector
    • Key point detection by using the Structure Tensor J and the Hassian Matrix H
  5. Greedy Snake

    • Use the gradient magnitude as image term
    • Calculate the three energy terms $E_{cont}$, $E_{curv}$, $E_{imag}$
    • Normalize the energy terms over the neighborhood to the intervall $[0, 1]$
    • Estimate the point in the neighborhood, that has minimum energy

Artificial Intelligence - MATLAB

  1. Nearest Neigbor

    • Matlab Introduction
    • Brute Force
  2. Linear Programming

  3. Perceptron

    • Pattern-by-Pattern Learning
    • Batch Learning
  4. Quadratic Programming

    • Optimization and Maximum Margin
  5. Duality (no code)

    • Dual Linear Program
    • Equilibrium Theorem
    • Strong Duality Theorem
    • Weak Duality Theorem
  6. Lagrange Multipliers

  7. Polynomial Kernels

  8. Simplified Sequential Minimal Optimization (SMO)

  9. Support Vector Regression (SVR)

  10. Neural Networks

    • Classification
    • Sigmoid Activation Function
    • Multi-Layer-Perceptron (MLP)

Medical Deep Learning - PyTorch

  1. Introduction to PyTorch

    • Tensor basic
    • Tensor playground
    • Autograd
    • CNN
    • ConvBlock
    • Pneumonia classification in x-ray torso
    • Dataset
    • Pytorch data tensor structure
    • Training
  2. 3D Semantic Segmentation

    Implementation of a deep 3D-CNN for multi-organ segmentation.

    Dataset: Learn2Reg Abdominal CT (20 training and 10 validation scans each manually labelled with 13 organs).

    • Affine transformation
    • 20 training and 10 validation scans each manually labelled with 13 organs
    • Training the FCN for segmentation
    • Segmentation using lite reduced atrous spatial pyramid pooling (LR-ASPP) (missing part)
  3. Electrocardiogram (ECG) Sequence Classification

    Classify (variable-length) ECG recordings of the PhysioNet/CinC Challenge into four rythm classes: normal sinus rhythms (N), atrial fibrillation (AF), other rhythms (O), noise signals (∼).

    • PhysioNet/CinC Challenge

    • Preprocessing

      To use the ecg signals with our neural nets normalize each recording s to have zero mean and unit variance.

    • Pre-trained 7 Layer ResNet (featnet) + Linear Classifier

    • 15 Layer ResNet + Linear Classifier

    • 15 Layer ResNet + LSTM + Linear Classifier

    • Transformer

  4. Image Registration

    Introduce deep-learning based image registration.

    • Computation of (joint) histograms and mutual information
    • Image Transformation
    • (Discrete) Correlation layer
    • Define feature network architecture
    • Training of MI-based registration
    • Modality Invariant Neighbourhood Descriptor (MIND) (missing part)
    • Learning global + local deformable multimodal registration (missing part)
  5. Weakly-Supervised Visualization

    Implement methods that allow to gain insights which parts of an input image to a Deep Neural Network are pivotal for its classification decision.

    • Implement dataset and visualize the given pancreas train & test data
    • Finetune a pretrained ResNet on the given data
    • Implement the Class Activation Mapping (CAM) method
    • Implement the guided backpropagation
    • Implementing the custom ReLU-Layer
  6. Model Distillation & Ternary Nets

    Implement methods that allow to compress deep learning models via model distillation and ternary weights. This enables the use of deep learning in medicine due to its real-time ability and implementation on weaker mobile devices.

    Data of Patch Camelyon (tupac16) Challenge is used.

    • Modify a pretrained VGG11_BN network for the given training data
    • Fine tuning
    • Network Pruning through increased Sparsity
    • Ternary weight approximation (missing part)

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