|V3 + Ü1 (6 ECTS credits)|
|1||10:30 - 12:00||TEMP1||Exercise||Exercise 4|
|2||10:30 - 12:00||TEMP2||Exercise||TensorFlow Tutorial|
- We are aware that there are currently some problems with registration for this course in the new rwth online system. We are working to fix them. Please be patient.
The goal of Machine Learning is to develop techniques that enable a machine to "learn" how to perform certain tasks from experience.
The important part here is the learning from experience. That is, we do not try to encode the knowledge ourselves, but the machine should learn it itself from training data. The tools for this are statistical learning and probabilistic inference techniques. Such techniques are used in many real-world applications. This lecture will teach the fundamental machine learning know-how that underlies such capabilities. In addition, we show current research developments and how they are applied to solve real-world tasks.
Example questions that could be addressed with the techniques from the lecture include
- Is this email important or spam?
- What is the likelihood that this credit card transaction is fraudulent?
- Does this image contain a face?
The class is accompanied by exercises that will allow you to collect hands-on experience with the algorithms introduced in the lecture.
There will be both pen&paper exercises and practical programming exercises (roughly 1 exercise sheet every 2 weeks). Please submit your solutions electronically through the L2P system.
We ask you to work in teams of 2-3 students.
The first half of the lecture will follow the book by Bishop. For the second half, we will use the Deep Learning book by Goodfellow as a reference.
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
- Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep learning, MIT Press, 2016
Wherever research papers are necessary for a deeper understanding, we will make them available in the L2P.
- Kevin Murphy, Machine Learning -- A Probabilistic Perspective, MIT Press, 2012.
- A comprehensive python tutorial which is quite long
- Gives a very basic introduction to python and control loops (A sub topic of above link)
- This subsection gives an overview of python data structures such as list, dictionaries etc. (Again a sub topic of above link)
- A basic numpy tutorial
- PyCharm, a Python IDE
|Introduction||Introduction, Probability Theory, Bayes Decision Theory, Minimizing Expected Loss|
|Prob. Density Estimation I||Parametric Methods, Gaussian Distribution, Maximum Likelihood|
|Prob. Density Estimation II||Bayesian Learning, Nonparametric Methods, Histograms, Kernel Density Estimation|
|Prob. Density Estimation III||Mixture of Gaussians, k-Means Clustering, EM-Clustering, EM Algorithm|
|Linear Discriminants I||Linear Discriminant Functions|
|Exercise 1||Python Tutorial, Probability Density, GMM, EM|
|Linear Discriminants II||Linear Discriminant Functions|
|Linear SVMs||Linear SVMs|
|Non-Linear SVMs||Non-Linear SVMs|
|Model Combination||Model Combination, AdaBoost, Exponential error, Sequential Additive Minimization|
|Exercise 2||Linear Discriminants, SVMs|
|Neural Networks I||Single-Layer Perceptron, Multi-Layer Perceptron, Mapping to Linear Discriminants, Error Functions, Regularization, Multi-layer Networks, Chain rule, Gradient Descent|
|Neural Networks II||Backpropagation, Computational Graphs, Stochastic Gradient Descent, Minibatch Learning, Optimizers (Momentum, RMS-Prop, AdaGrad, Adam)|
|Tricks of the Trade||Initialization (Glorot, He), Nonliniearities, Drop-out, Batch Normalization, Learning Rate Schedules|
|Exercise 3||Adaboost, Deep Learning Companion Slides (preparation for Exercise 4)|
|Convolutional Neural Networks I||CNNs, Convolutional Layers, Pooling Layers, LeNet|
|Convolutional Neural Networks II||ImageNet Challenge, Notable Architectures, AlexNet, VGGNet, Inception, Visualizing CNNs|
|Exercise 4||Softmax, Backpropagation, Deep Learning Implementation from scratch|
|TensorFlow Tutorial||TensorFlow Tutorial|