Long name	Machine Learnig
Approving CModule	ML_BaTIN
Responsible	Prof. Dr. Lothar Thieling Professor Fakultät IME
Level	Bachelor
Semester in the year	winter semester
Duration	Semester
Hours in self-study	78
ECTS	5
Professors	Prof. Dr. Lothar Thieling Professor Fakultät IME
Requirements	basic skills in Java and/or C basic skills in analysis and linear algebra
Language	German
Separate final exam	Yes

fundamentals
types of learning
simple classifiers
simple predictors (Equalizer alias Linear Regression)
challenges in learning
linear regression as the simple predictor
linear regression as the simple classifier
training data (handling, analysis, processing)
gradient descent
quality measures
learning curve
multi-class classifier based on binary classifiers
multi-label-classification
logistic regression

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simple neuronale Netze
the artificial neuron as a simple classifier
operation
activation function
bias
training a neuron
multi-layer-perceptron
operation
purposes of the layers
backpropagation training algorithm
tools for creating and training simple neural networks and handling training data
handling, analysis and preparation of training data
creating and configuring neural networks
training neural networks
verification of trained networks

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Deep Neural Networks (DNNs)
basic problems
vanishing or exploding gradients
high training times
overfitting
solutions for the probblems mentioned above
appropriate initialization of the weights, non-saturating activation function, gradient clipping
accelerated optimization procedures, reuse of pre-trained layers
regularization to avoid overfitting
tools for creating and training DNNs
handling, analysis and preparation of training data
creating and configuring neural networks
training of neural networks
verification and validation trained networks

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Convolutional Neural Networks (CNNs)
idea
architecture
convolutional layer
pooling layer
convolution as a basic operator for training and detection
architectures of CNNs for different problems
tools for implementation and training CNNs

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Recurrent Neural Networks (RNNs)
idea
recurrent neurons
training of RNNs and Deep RNNs
Long Short Term Memory
architectures of RNNs for different problems
tools for implementation and training deep CNNs

the presented neural networks
specify
describe
evaluate the pros and cons

solving problems using tools
for handling, analysis and preparation of the training data
for implementationion, verification, validation and training of all neural presented networks

Type	Attendance (h/Wk.)
Lecture	2
Exercises (whole course)	0
Exercises (shared course)	0
Tutorial (voluntary)	0

none

purposeful handling of the tools

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deal with complex tasks in a small team

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derive complex solutions that can be implemented using neural networks

Type	Attendance (h/Wk.)
Practical training	2
Tutorial (voluntary)	0

none