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.
Testimonials
Details
| Topic | Introductory course to machine learning |
| Date | March 9 – 13 (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 9 – 13 (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 Alpina hotel
09.30:
Gondola up
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:
Future of Machine Learning
Course by Andreas Krause, ETH Zürich Professor
Hands-on session
Backup session or challenge
Keynote speaker:
an overview of crrent 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
09.00:
Arrival at Alpina hotel
09.30:
Gondola up
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:
Future of Machine Learning
Course by Andreas Krause, ETH Zürich Professor
Hands-on session
Backup session or challenge
Keynote speaker:
an overview of crrent research directions in ML.
Course by Andreas Krause, ETH Zürich Professor
Future of Machine Learning
Friday, March x
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 Alpina 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
Instructors
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 Fiescheralp in the canton of Valais. Course participants reside in the Alpina Hotel.
images credits:
Alpina Hotel
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
Contact us :
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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. Read more…