The Swiss Data Science Centre is pleased to announce that a one-week introductory course to Machine Learning will be held from March 9 to 13, 2020 at the Alpina Hotel located in Fiescheralp in the canton of Valais.
Basic experience in Python programming
▪ Data types: lists, arrays, dictionaries
▪ Control structures: if/else statements, for/while loops
▪ Defining and calling functions
While some Python knowledge is recommended, people who are very experienced in other languages are usually able to learn the necessary Python skills on the go.
▪ Linear algebra: matrix-vector multiplication, matrix eigen-decomposition, vector norms
▪ Calculus/analysis : derivative/gradient of a function, minimum, maximum, inflexion point.
▪ Statistics and probability: Bayes rule, probability function, density function.
While the trainers will try their best to help people with individual gaps in some of the above topics, being unfamiliar with the majority of them will make it very hard to follow the course.
Topics covered by the course
Here is an indicative list of the topic covered by the course. Some of them will only be broadly discussed:
- Supervised learning: regression, classification, overfitting, cross-validation
- Basic of optimization: gradient descent, backpropagation, stochastic gradient descent
- Unsupervised learning: dimensionality reduction clustering
- Algorithms: SVM, Random Forest. K-MEANS, PCA
- Deep learning: Neural Network (NN), Fully connected NN, Convolutional NN, traditional NN tricks
Course information available at: https://carvingthroughdata.ch/
A 2017 ETH Board national Data Science initiative resulted in the creation of a unique joint venture between EPFL and ETH Zurich: the Swiss Data Science Center. The Center’s mission is to accelerate the use of data science and machine learning techniques within academic disciplines of the ETH Domain, the Swiss academic community at large, and the industrial sector. A multi-disciplinary team of senior data scientists and experts in domains such as personalized health and medicine, earth and environmental science, social science and digital humanities, as well as economics enables collaboration on both academic and industrial projects. This unique positioning, at the crossroad of academic excellence and fast-paced business environments agility is key in making the complex data science journey simple.