Suchergebnis: Katalogdaten im Frühjahrssemester 2020

Nummer	Titel	Typ	ECTS	Umfang	Dozierende
DAS in Data Science
Kernfächer
Einführungskurse
252-0220-00L	Introduction to Machine Learning Limited number of participants. Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact studiensekretariat@inf.ethz.ch	W	8 KP	4V + 2U + 1A	A. Krause
Kurzbeschreibung	The course introduces the foundations of learning and making predictions based on data.
Lernziel	The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project.
Inhalt	- Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent) - Linear classification: Logistic regression (feature selection, sparsity, multi-class) - Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor - Neural networks (backpropagation, regularization, convolutional neural networks) - Unsupervised learning (k-means, PCA, neural network autoencoders) - The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference) - Statistical decision theory (decision making based on statistical models and utility functions) - Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions) - Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE) - Bayesian approaches to unsupervised learning (Gaussian mixtures, EM)
Literatur	Textbook: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
Voraussetzungen / Besonderes	Designed to provide a basis for following courses: - Advanced Machine Learning - Deep Learning - Probabilistic Artificial Intelligence - Seminar "Advanced Topics in Machine Learning"
401-3632-00L	Computational Statistics	W	8 KP	3V + 1U	M. H. Maathuis
Kurzbeschreibung	We discuss modern statistical methods for data analysis, including methods for data exploration, prediction and inference. We pay attention to algorithmic aspects, theoretical properties and practical considerations. The class is hands-on and methods are applied using the statistical programming language R.
Lernziel	The student obtains an overview of modern statistical methods for data analysis, including their algorithmic aspects and theoretical properties. The methods are applied using the statistical programming language R.
Voraussetzungen / Besonderes	At least one semester of (basic) probability and statistics. Programming experience is helpful but not required.

Capstone Project
Nummer	Titel	Typ	ECTS	Umfang	Dozierende
266-0100-00L	Capstone Project Only for DAS in Data Science.	O	8 KP	17A	F. Perez Cruz, O. Verscheure, T. Hofmann
Kurzbeschreibung	The capstone project is part of the DAS in Data Science and is an opportunity to apply the knowledge acquired in the program in an independent, real-world project.
Lernziel	To apply the knowledge acquired in the program in an independent, real-world project.
Inhalt	The capstone project can be done under the supervision of the Swiss Data Science Center, or of any core or adjunct faculty of Data Science. The project has to be finished within 6 months. Deadline for a project the following semester conducted at the SDSC is mid June/mid December.

Seite 1 von 1