Course Manual ML
Machine Learnig
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 |
| Valid from | winter semester 2022/23 |
| 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 |
Literature
| Géron, Aurélien, Hands-On Machine Learning with Scikit-Learn and TensorFlow, O'Reilly Medi |
Final exam
| Details | The students should demonstrate the following competences in an oral exam: 1.) Safe handling of basic concepts and mechanisms. 2.) Analyze problems in the field of machine learning and solve them with suitable methods. 3.) Analyze existing solutions and explain the used algorithmic and theory. |
|---|---|
| Minimum standard | At least 50% of the total number of points |
| Exam Type | EN mündliche Prüfung, strukturierte Befragung |
Learning goals
| Goal type | Description |
|---|---|
| Knowledge | 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 |
| Knowledge | 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 |
| Knowledge | 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 |
| Knowledge | 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 |
| Knowledge | 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 |
| Skills | 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 |
Expenditure classroom teaching
| Type | Attendance (h/Wk.) |
|---|---|
| Lecture | 2 |
| Exercises (whole course) | 0 |
| Exercises (shared course) | 0 |
| Tutorial (voluntary) | 0 |
Special requirements
| none |
| Accompanying material | lecture foils (electronic), undefined, self-study tutorials for the tools |
|---|---|
| Separate exam | No |
Learning goals
| Goal type | Description |
|---|---|
| Skills | purposeful handling of the tools |
| Skills | deal with complex tasks in a small team |
| Skills | derive complex solutions that can be implemented using neural networks |
Expenditure classroom teaching
| Type | Attendance (h/Wk.) |
|---|---|
| Practical training | 2 |
| Tutorial (voluntary) | 0 |
Special requirements
| none |
| Accompanying material | problem and task description (electronic), tool chain for neural networks, self-study tutorials for the tools |
|---|---|
| Separate exam | No |
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