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• SS18_BeginnersWorkshop

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