Carving Through Data
A different introductory course to Machine Learning9-13 March 2020, Fiescheralp, Switzerland
Carving Through Data
A different introductory course to Machine Learning9-13 March 2020, Fiescheralp, Valais, Switzerland
Who we are
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.
Who we are
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.
Our concept
In this novel introductory machine learning course we take you skiing. And we are serious about the skiing – so serious that we make it an integral part of the course. When hitting the slopes, you will be collecting data through three lightweight IMU sensors. During extensive hands-on sessions you will perform data exploration, build regression models or identify clusters, all based on your own sensor data. This is not only more motivating than analyzing yet another overly curated example dataset, it is also much closer to the real-world problems that data scientists are facing every day.
Our concept
In this novel introductory machine learning course we take you skiing. And we are serious about the skiing – so serious that we make it an integral part of the course. When hitting the slopes, you will be collecting data through three lightweight IMU sensors. During extensive hands-on sessions you will perform data exploration, build regression models or identify clusters, all based on your own sensor data. This is not only more motivating than analyzing yet another overly curated example dataset, it is also much closer to the real-world problems that data scientists are facing every day.
Why?
The importance of data science and artificial intelligence (AI) technologies and their potential impact on economy and society is nowadays widely recognized. To fully grasp the digitalization opportunity, and sometimes purely to survive, companies have to evolve and adapt at a pace they have never experienced before.
In order to navigate today’s digital age, many traditional corporations welcome a new type of experts: data scientists.
These skilled specialists are highly sought-after professionals and access to them is critical to an ever-growing part of the Swiss industry. In order to retain them and allow them to develop, companies need to offer their data experts every opportunity to learn and grow. However, these opportunities are sometimes limited within a corporation that is still at the dawn of its digital transformation journey.
Through participation in the recently established MSc programs of both EPFL and ETH Zürich, the Swiss Data Science Center (SDSC) is playing a vital role in educating tomorrows’ data scientists for Switzerland. Of equal importance to the SDSC is the continued education of professionals in selected topics related to data science. This is why the center is involved in DAS and CAS certificate programs offered by the EPFL and ETH Zürich.
However, not everyone can dedicate several months to continuous learning.
For this reason, we have created “Carving through Data”, a one-week introductory course to Machine Learning for professionals with backgrounds in quantitative disciplines such as mathematics, engineering or natural sciences.
Why?
The importance of data science and artificial intelligence (AI) technologies and their potential impact on economy and society is nowadays widely recognized. To fully grasp the digitalization opportunity, and sometimes purely to survive, companies have to evolve and adapt at a pace they have never experienced before.
In order to navigate today’s digital age, many traditional corporations welcome a new type of experts: data scientists.
These skilled specialists are highly sought-after professionals and access to them is critical to an ever-growing part of the Swiss industry. In order to retain them and allow them to develop, companies need to offer their data experts every opportunity to learn and grow. However, these opportunities are sometimes limited within a corporation that is still at the dawn of its digital transformation journey.
Through participation in the recently established MSc programs of both EPFL and ETH Zürich, the Swiss Data Science Center (SDSC) is playing a vital role in educating tomorrows’ data scientists for Switzerland. Of equal importance to the SDSC is the continued education of professionals in selected topics related to data science. This is why the center is involved in DAS and CAS certificate programs offered by the EPFL and ETH Zürich.
However, not everyone can dedicate several months to continuous learning.
For this reason, we have created “Carving through Data”, a one-week introductory course to Machine Learning for professionals with backgrounds in quantitative disciplines such as mathematics, engineering or natural sciences.
Testimonials
In summary
| Topic | Introductory course to machine learning |
| Date | March 9 – 13 (5 course days) |
| Participants | The number of participants is limited to 20. |
In summary
| Topic | Introductory course to machine learning |
| Date | March 9 – 13 (5 course days) |
| Participants | The number of participants is limited to 20. |
Tuition fees
early registration* |
regular registration |
| 1800.- CHF | 2000.- CHF |
Tuition fees
early registration* |
regular registration |
| 1800.- CHF | 2000.- CHF |
Hotel costs
Price CHF |
4 days |
5 days (INCLUDING SUNDAY NIGHT) |
Room |
520.- | 650.- |
Half board |
140.- | 175.- |
Touristtax |
10.- | 12.50 |
Total |
670.- | 837.50 |
With 10 % early registration discount* |
604.- | 755.- |
* Early bird rate applies until October 31, 2019
Hotel costs
Price CHF |
4 days |
5 days (INCLUDING SUNDAY NIGHT) |
Room |
520.- | 650.- |
Half board |
140.- | 175.- |
Touristtax |
10.- | 12.50 |
Total |
670.- | 837.50 |
With 10 % early registration discount* |
604.- | 755.- |
* Early bird rate applies until October 31, 2019
Prerequisites
– 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.
– Mathematic
- Linear algebra: matrix-vector multiplication, matrix eigen-decomposition, vector norms
- Calculus/analysis : derivative/gradient of a function, minimum, macimum, inflexion point.
- Statistics and probability: Bayes rule, probability function, density function.
Remark: While we will try our 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.
– Skiing is not required. Data will be provided for non-skiers.
Skipass (optional) 213.- for 5 days
Note that skiing participants are required to bring their own equipment or rent at a local sport shop. While the course is built on data collected while skiing, non-skiers can also join the course (data will be provided to them). No ski tuition will be provided as part of the course. Please contact us should you have any question.
Prerequisites
– 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.
– Mathematics
- 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 we will try our 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.
– Skiing is not required. Data will be provided for non-skiers.
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
Venue
The course will be held in Fiescheralp in the canton of Valais. Course participants reside in the Alpina Hotel.
images credits:
Alpina Hotel
Venue
The course will be held in Laax in the canton of Graubünden. Course participants reside in the Signina Hotel where a conference room for evening hacking sessions is available.
images credits:
www.signinahotel.com
Program
09.30:
Beginning of the program
Course A
Introduction do basic data science software tools. (Jupyter, Numpy, Pandas, Sckit-learn, Git)
Course B
Data exploration and plotting
Practical work:
understanding and exploring a data pipeline, data extraction and plotting
SDSC – speaker:
Course C
Supervised learning 1. (Linear regression, basic optimization)
Course D
Dataset preparation. (Overfitting, dataset splitting)
Hands-on session
Implementation of a regression algorithm to predict the skier speed from other variables
SDSC – Tutor:
Course E
Supervised learning 2. (Classification)
Course F
Un-supervised learning. (Clustering, k-means, basic embedding, PCA)
Hands-on session
Group skier with similar ski styles using unsupervised learning (k-means). Create an embedding of the participants ski data to extract their differences in ski styles
SDSC – Tutor:
Course G
Introduction to deep learning. (What is deep learning? Why does it work? When should it be used? What are its limitations?)
Course H
Convolutional neural networks
Hands-on session
Learn features and improve the previous regression/classification tasks using convolutional neural networks
SDSC – Speaker:
Backup course session or one advanced ML topic such as (Generative Adversarial Network, Reinforcement Learning, Graph neural network, Natural language processing).
Hands-on session
backup session or challenge
Keynote speaker:
An overview of current research directions in ML.
Typical schedule
(7.5 + hours of tuition per day)
| 08:00 -09:45 | Course A |
| 09:45 – 10:00 | Coffee Break |
| 10:00 – 11:45 | Course B |
| 11:45 – 15:00 | Lunch break (on the resort) |
| 15:00 – 19:00 | Hands-on session and data acquisition (ski) |
| 19:30 – 20:30 | Dinner |
| 21:00 | Open end: Hacking/questions in the lobby |
Program
09.30:
Beginning of the program
Course A
Introduction do basic data science software tools. (Jupyter, Numpy, Pandas, Sckit-learn, Git)
Course B
Data exploration and plotting.
Practical work:
understanding and exploring a data pipeline, data extraction and plotting
SDSC – speaker:
Course C
Supervised learning 1. (Linear regression, basic optimization)
Course D
Dataset preparation. (Overfitting, dataset splitting)
Hands-on session
Implementation of a regression algorithm to predict the skier speed from other variables
SDSC – Tutor:
Course E
Supervised learning 2. (Classification)
Course F
Un-supervised learning. (Clustering, k-means, basic embedding, PCA)
Hands-on session
Group skier with similar ski styles using unsupervised learning (k-means). Create an embedding of the participants ski data to extract their differences in ski styles
SDSC – Tutor:
Course G
Introduction to deep learning. (What is deep learning? Why does it work? When should it be used? What are its limitations?)
Course H
Convolutional neural networks
Hands-on session
Learn features and improve the previous regression/classification tasks using convolutional neural networks
SDSC – Tutor:
Backup course session or one advanced ML topic such as (Generative Adversarial Network, Reinforcement Learning, Graph neural network, Natural language processing).
Hands-on session
backup session or challenge
Keynote speaker:
An overview of current research directions in ML.
Optional
Join us one day earlier to collect a large bunch of your own data using the snow-cookie sensors on the slopes of Fiescheralp before the actual course starts.
Typical schedule
(7.5 + hours of tuition per day)
| 08:00 -09:45 | Course A |
| 09:45 – 10:00 | Coffee Break |
| 10:00 – 11:45 | Course B |
| 11:45 – 15:00 | Lunch break (on the resort) |
| 15:00 – 19:00 | |
| 19:30 – 20:30 | Dinner |
| 21:00 |
Main Instructors
Instructors
Sensors
Sensors
The sensors used (named Snowcookies) are produced in Switzerland by a Swiss-Polish start-up (http://snowcookiesports.com). Each sensor measures acceleration, angular velocity and orientation. The sensors are connected to a mobile phone which records the data. Three sensors per skier (left ski, right ski, chest) together with GPS and air pressure data measured by the phone allow for a detailed analysis of the skier’s environment and technique.
Contact us :
About us
We accompany the academic community and the industrial sector in their data science journey, putting to work AI and ML and facilitating the multidisciplinary exchange of data and knowledge…