PDF Course Catalog Deutsche Version: ML

Version: 1 | Last Change: 23.09.2019 12:26 | Draft: 0 | Status: vom verantwortlichen Dozent freigegeben

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 |

Géron, Aurélien, Hands-On Machine Learning with Scikit-Learn and TensorFlow, O'Reilly Medi

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

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

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

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

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

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

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

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

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

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

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 |

keine/none

none

lecture foils (electronic)

self-study tutorials for the tools

self-study tutorials for the tools

none

purposeful handling of the tools

deal with complex tasks in a small team

derive complex solutions that can be implemented using neural networks

deal with complex tasks in a small team

derive complex solutions that can be implemented using neural networks

Type | Attendance (h/Wk.) |
---|---|

Practical training | 2 |

Tutorial (voluntary) | 0 |

keine/none

none

problem and task description (electronic)

tool chain for neural networks

self-study tutorials for the tools

tool chain for neural networks

self-study tutorials for the tools

none

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