== Beginners Workshop Machine Learning ==

 || '''From:''' || 2018-09-03||
 || '''To:''' || 2018-09-14||
 || '''Lecture time:''' || 8:45 - 12:00, 13:30 - 17:00 (approx.) ||
 || '''Exam:''' ||  2018-09-24||
 || '''Room:''' || MAR 4.063, Marchstr. 23 ||
 || '''Organisation:''' || Seulki Yeom: yeom@tu-berlin.de, Philipp Seegerer: philipp.seegerer@tu-berlin.de, David Lassner: lassner@tu-berlin.de ||
 ||'''Language'''|| English ||
 ||'''Application deadline'''|| June 15th, 2018 ||


== Enrollment / Limited number of participants ==

If you intend to participate, please send an e-mail to lassner@tu-berlin.de with title "Beginners Workshop Enrollment" and this text:
{{{
Name: Your name
Matr.Nr: Your student ID (Matrikelnummer)
Degree: The degree you are enrolled in and want to use this course for.
TU student: Yes/No (Are you a enrolled as a regular student at TU Berlin?)
Other student: If you are not a regular student, please write your status.
ML1: Yes/No (Did you take the course Machine Learning 1 at TU Berlin?)
Other ML course: If you did not take ML1 at TU Berlin, please write if you took any equivalent course.
}}}

Participation spots are mostly assigned on a random basis. Please keep in mind that auditing students and Nebenhörer can only participate if less than the maximum number of regular TU students register for the course (http://www.studsek.tu-berlin.de/menue/studierendenverwaltung/gast_und_nebenhoererschaft/parameter/en/).

(temporary) Workshop Lecture topics are:

1. Clustering, mixtures, density estimation
 * Density estimation: kernel density estimation, Parzen windows, parametric density
 * K means clustering
 * Gaussian mixture models, EM algorithm
 * Curse of dimensionality

2. Manifold learning
 * LLE
 * Embeddings (RBF)
 * Multidimensional scaling
 * tSNE

3. Bayesian Methods
 * What is learning?
 * Frequentist vs Bayes
 * Bayes rule
 * Naive Bayes
 * Bayesian linear regression
 * Bayesian/Akaike information criterion, Occam's razor

4. Classical and linear methods
 * Matrix factorization
 * Logistic regression
 * Regularization, Lasso, Ridge regression
 * Fisher's Linear discriminant
 * Gradient descent
 * Decision boundaries

5. Support Vector Machine
 * Linear SVM
 * Linear separability, maximum margin and soft margin
 * Duality in optimization, KKT conditions
 * SVM for regression
 * Multi-class SVM
 * Applications

6. Kernels
 * Feature transformations
 * Kernel trick
 * Nadaraya-Watson kernel regression

7. Neural Networks
 * Rosenblatt's Perceptron
 * Multi layer perceptron
 * Motivation with logistic regression
 * Backpropagation, (Stochastic) (Minibatch) gradient descent
 * Convolutional NNs
 * Famous Conv net architectures: AlexNet, GoogleNet, ResNet etc.
 * Recurrent NNs
 * Applications
 * Practical recommendations for Training of DNNs, hyperparameter tuning