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About this Lecture
Introduction
Lecture
1. Linear Models
2. Gaussian Mixture Models
3. Bayesian methods
4. Optimization
5. Tricks of Optimization
6. Support Vector Machines
7. Gaussian Processes
8. Gradients
9. Multilayer Perceptron
10. Convolutional Neural Networks
11. Recurrent Models
12. Encoder-Decoder Models
Exercise
1. Linear and Logistic Regression
2. Bayesian Linear Models
3. Optimization
4. Support Vector Machines
5. Gaussian Processes
6. Convolutional Neural Networks
7. Recurrent Neural Networks
Miscellaneous
Admin
Books
References
Preliminary Knowledge
Software Infrastructure
Practical Exam WS22/23
Frequently Asked Questions
Repository
Open issue
Index
