Carving Through Data
A different introductory course to Machine Learning11-15 March 2019, Laax, Switzerland
See you next year!
Carving Through Data
A different introductory course to Machine Learning11-15 March 2019, Laax, Switzerland
See you next year!
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.
Details
| Topic | Introductory course to machine learning |
| Date | March 11 – 15 (5 course days) |
| Costs | CHF 3500 per participant (tuition fee, accommodation, ski pass) |
| Participants | The number of participants is limited to 20. |
| Prerequisites (skiing) | Ability to ski independently in a ski resort on blue and red runs. |
| Prerequisites (academic) | Basic knowledge of linear algebra and python programming. |
Participants are required to come with their own ski equipment. Please contact us should you have any question.
Details
| Topic | Introductory course to machine learning |
| Date | March 11 – 15 (5 course days) |
| Costs | CHF 3500 per participant (tuition fee, accommodation, ski pass) |
| Participants | The number of participants is limited to 20. |
| Prerequisites (skiing) | Ability to ski independently in a ski resort on blue and red runs. |
| Prerequisites (academic) | Basic knowledge of linear algebra and python programming. |
Participants are required to come with their own ski equipment. Please contact us should you have any question.
Program
09.00:
Arrival at Signina hotel
09.30:
Gondola up
Course A
Introduction to basic data science software tools, data exploration and plotting.
Course B
Introduction to signal processing for time series
Hands-on session
Ski data acquisition, data importation and exploration, simple statistical analysis, information extraction and plotting
SDSC – Tutor:
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 and how 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:
Future of Machine Learning
Course by Andreas Krause, ETH Zürich Professor
Hands-on session
Data science challenge
Schedule
| 8:30 | Gondola up |
| 9:00 – 10:00 | Course A |
| 10:00 – 10:45 | Break (ski or question time) |
| 10:45 – 11:45 | Course B |
| 11:45 – 12:00 | Presentation of the hands-on session |
| 12:00 – 13:30 | Lunch break (on the resort) |
| 13:00 – 18:30 | Hands-on session and data acquisition (ski) |
| 19:00 – 20:00 | Dinner |
| 20:00 – 21:00 | Questions and feedback time |
Program
09.00:
Arrival at Signina hotel
09.30:
Gondola up
Course A
Introduction to basic data science software tools, data exploration and plotting.
Course B
Introduction to signal processing for time series
Hands-on session
Ski data acquisition, data importation and exploration, simple statistical analysis, information extraction and plotting
SDSC – Tutor:
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 and how 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:
Future of Machine Learning
Course by Andreas Krause, ETH Zürich Professor
Hands-on session
Data science challenge
Course by Andreas Krause, ETH Zürich Professor
Future of Machine Learning
Friday, March 15
2-5 pm
- 8:30 : Gondola up
- 9:00 – 10:00 : Course A
- 10:00 – 10:45 : Break (ski or question time)
- 10:45 – 11:45 : Course B
- 11:45 – 12:00 : Presentation of the hands-on session
- 12:00 – 13:30 : Lunch break (on the resort)
- 13:00 – 18:30 : Hands-on session and data acquisition (ski)
- 19:00 – 20:00 : Dinner
- 20:00 – 21:00 : Questions and feedback time
Schedule
| 8:30 | Gondola up |
| 9:00 – 10:00 | Course A |
| 10:00 – 10:45 | Break (ski or question time) |
| 10:45 – 11:45 | Course B |
| 11:45 – 12:00 | Presentation of the hands-on session |
| 12:00 – 13:30 | Lunch break (on the resort) |
| 13:00 – 18:30 | Hands-on session and data acquisition (ski) |
| 19:00 – 20:00 | Dinner |
| 20:00 – 21:00 | Questions and feedback time |
Program
09.00:
Arrival at Signina hotel
09.30:
Gondola up
Course A
Introduction to basic data science software tools, data exploration and plotting.
Course B
Introduction to signal processing for time series
Hands-on session
Ski data acquisition, data importation and exploration, simple statistical analysis, information extraction and plotting
SDSC – Tutor:
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 and how 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:
Future of Machine Learning
Course by Andreas Krause, ETH Zürich Professor
Hands-on session
Data science challenge
Course by Andreas Krause,
ETH Zürich Professor
Future of Machine Learning
Friday, March 15
2-5 pm
Schedule
| 8:30 | Gondola up |
| 9:00 – 10:00 | Course A |
| 10:00 – 10:45 | Break (ski or question time) |
| 10:45 – 11:45 | Course B |
| 11:45 – 12:00 | Presentation of the hands-on session |
| 12:00 – 13:30 | Lunch break (on the resort) |
| 13:00 – 18:30 | Hands-on session and data acquisition (ski) |
| 19:00 – 20:00 | Dinner |
| 20:00 – 21:00 | Questions and feedback time |
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.
Main Instructors
Andreas Bleuler
Sr. Computer Scientist
- PhD in Computational Astrophysics
- Senior Software Engineer at SDSC
- High school teachers degree for physics
- Expert level qualifications from the Association of Swiss Ski Schools and J+S.
- 15 years of teaching experience in a Swiss ski school
Nathanaël Perraudin
Sr. Data Scientist
- PhD in Data Science
- Senior Data Scientist at SDSC
- 10 years of teaching experience in a Swiss ski school
- Degree 1 qualification from the Association of Swiss Ski Schools
Instructors
Andreas Bleuler
Sr. Computer Scientist
- PhD in Computational Astrophysics
- Senior Software Engineer at SDSC
- High school teachers degree for physics
- Expert level qualifications from the Association of Swiss Ski Schools and J+S.
- 15 years of teaching experience in a Swiss ski school
Nathanaël Perraudin
Sr. Data Scientist
- PhD in Data Science
- Senior Data Scientist at SDSC
- 10 years of teaching experience in a Swiss ski school
- Degree 1 qualification from the Association of Swiss Ski Schools
Keynote Speaker
Andreas Krause
Professor of Computer Science at ETH Zürich
Andreas Krause is an Associate Professor of Computer Science at ETH Zurich, where he leads the Learning & Adaptive Systems Group. Before that he was an Assistant Professor of Computer Science at Caltech. He received his Ph.D. in Computer Science from Carnegie Mellon University (2008) and his Diplom in Computer Science and Mathematics from the Technical University of Munich, Germany (2004). He is a Microsoft Research Faculty Fellow and a Kavli Frontiers Fellow of the US National Academy of Sciences. He received an ERC Starting Investigator grant, the Deutscher Mustererkennungspreis, an NSF CAREER award, the Okawa Foundation Research Grantrecognizing top young researchers in telecommunications as well as the ETH Golden Owl teaching award. His research on machine learning and adaptive systems has received awards at several premier conferences and journals. Andreas Krause is regularly serving as Area Chair or Senior Program Committee member for ICML, NIPS, AAAI and IJCAI, and as Action Editor for the Journal of Machine Learning Research.
Keynote speaker
Andreas Krause
Professor of Computer Science at ETH Zürich
Andreas Krause is an Associate Professor of Computer Science at ETH Zurich, where he leads the Learning & Adaptive Systems Group. Before that he was an Assistant Professor of Computer Science at Caltech. He received his Ph.D. in Computer Science from Carnegie Mellon University (2008) and his Diplom in Computer Science and Mathematics from the Technical University of Munich, Germany (2004). He is a Microsoft Research Faculty Fellow and a Kavli Frontiers Fellow of the US National Academy of Sciences. He received an ERC Starting Investigator grant, the Deutscher Mustererkennungspreis, an NSF CAREER award, the Okawa Foundation Research Grantrecognizing top young researchers in telecommunications as well as the ETH Golden Owl teaching award. His research on machine learning and adaptive systems has received awards at several premier conferences and journals. Andreas Krause is regularly serving as Area Chair or Senior Program Committee member for ICML, NIPS, AAAI and IJCAI, and as Action Editor for the Journal of Machine Learning Research.
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
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
Course Venue
For the regular course sessions, we have chosen a unique co-working space (bridge.laax.com), located on “Crap Sogn Gion” at the very center of the Laax ski area.
images credits:
www.instagram.com/thebridge_galaaxy
www.laax.com/en/ski-resort/galaaxy
Course Venue
For the regular course sessions, we have chosen a unique co-working space (bridge.laax.com), located on “Crap Sogn Gion” at the very center of the Laax ski area.
images credits:
www.instagram.com/thebridge_galaaxy
www.laax.com/en/ski-resort/galaaxy
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.
