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 '''Organisation:'''  Seulki Yeom: seulki.yeom@tuberlin.de, Philipp Seegerer philipp.seegerer@campus.tuberlin.de, David Lassner lassner@tuberlin.de    '''Organisation:'''  Seulki Yeom: yeom@tuberlin.de, Philipp Seegerer: philipp.seegerer@tuberlin.de, David Lassner: lassner@tuberlin.de  
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'''Application deadline''' June 15th, 2018  == Enrollment / Limited number of participants == If you intend to participate, please send an email to lassner@tuberlin.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.tuberlin.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 * Multiclass SVM * Applications 6. Kernels * Feature transformations * Kernel trick * NadarayaWatson 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 
Beginners Workshop Machine Learning
From:
20180903
To:
20180914
Exam:
20180924
Organisation:
Seulki Yeom: yeom@tuberlin.de, Philipp Seegerer: philipp.seegerer@tuberlin.de, David Lassner: lassner@tuberlin.de
Language
English
Application deadline
June 15th, 2018
Enrollment / Limited number of participants
If you intend to participate, please send an email to lassner@tuberlin.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.tuberlin.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
 Multiclass SVM
 Applications
6. Kernels
 Feature transformations
 Kernel trick
 NadarayaWatson 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