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---
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# About us
---
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## About Scientific IT Services (SIS)
---
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## About Scientific IT Services (SIS)
- IT Services close to research
---
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## About Scientific IT Services (SIS)
- IT Services close to research
- Zoo of experts with diverse background
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## About Scientific IT Services (SIS)
- IT Services close to research
- Zoo of experts with diverse background
### Here today in real:
* Dr. Tarun Chadha
* Dr. Mikolaj Rybinski
* Dr. Uwe Schmitt
---
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## Our Services
- Scientific Computing Services: Euler, ...
---
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## Our Services
- Scientific Computing Services: Euler, ...
- Data Science Support
---
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## Our Services
- Scientific Computing Services: Euler, ...
- Data Science Support
- Software Development: openBIS, ELN, ...
---
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## Our Services
- Scientific Computing Services: Euler, ...
- Data Science Support
- Software Development: openBIS, ELN, ...
- Scientific Visualization
---
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## Our Services
- Scientific Computing Services: Euler, ...
- Data Science Support
- Software Development: openBIS, ELN, ...
- Scientific Visualization
- Consulting and Training: Code Clinics, ...
---
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## Our Services
- Scientific Computing Services: Euler, ...
- Data Science Support
- Software Development: openBIS, ELN, ...
- Scientific Visualization
- Consulting and Training: Code Clinics, ...
- Research Data Management
---
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## Our Services
- Scientific Computing Services: Euler, ...
- Data Science Support
- Software Development: openBIS, ELN, ...
- Scientific Visualization
- Consulting and Training: Code Clinics, ...
- Research Data Management
- Research Data Analysis
---
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## Our Services
- Scientific Computing Services: Euler, ...
- Data Science Support
- Software Development: openBIS, ELN, ...
- Scientific Visualization
- Consulting and Training: Code Clinics, ...
- Research Data Management
- Research Data Analysis
- Personalized Health Data Services
---
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<span style="font-size: 200%; color: #1F407A;">
Contact us !
<br/>
<br/>
https://sis.id.ethz.ch/
</span>
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# About the course
---
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# About the course
* First version, might be a bit rough, not sure if time planning is good.
---
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# About the course
* First version, might be a bit rough, not sure if time planning is good.
* Pragmatic approach, little math.
---
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# About the course
* First version, might be a bit rough, not sure if time planning is good.
* Pragmatic approach, little math.
* Introduction only !
---
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# About the course (2)
What will you learn ?
* Basic concepts of Machine Learning (ML).
---
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# About the course (2)
What will you learn ?
* Basic concepts of Machine Learning (ML).
* More classical machine learning than artificial neural networks.
---
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# About the course (2)
What will you learn ?
* Basic concepts of Machine Learning (ML).
* More classical machine learning than artificial neural networks.
* General overview of supervised learning and related methods.
---
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# About the course (2)
What will you learn ?
* Basic concepts of Machine Learning (ML).
* More classical machine learning than artificial neural networks.
* General overview of supervised learning and related methods.
* How to start with ML using `scikit-learn` and `Keras`.
---
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# About the course (3)
What will you NOT learn?
* How to program with Python.
---
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# About the course (3)
What will you NOT learn?
* How to program with Python.
* How exactly ML methods work.
---
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# About the course (3)
What will you NOT learn?
* How to program with Python.
* How exactly ML methods work.
* Unsupervised learning methods.
---
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# Schedule
---
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# Day 1
- Chapter 0: Quick Introduction to numpy, pandas, matplotlib
---
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# Day 1
- Chapter 0: Quick Introduction to numpy, pandas, matplotlib
- Chapter 1: General Introduction to Machine Learning
---
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# Day 1
- Chapter 0: Quick Introduction to numpy, pandas, matplotlib
- Chapter 1: General Introduction to Machine Learning
- Chapter 2: Introduction to Classification
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# Day 1
- Chapter 0: Quick Introduction to numpy, pandas, matplotlib
- Chapter 1: General Introduction to Machine Learning
- Chapter 2: Introduction to Classification
- Chapter 3: Overfitting and Cross Validation
---
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# Day 1
- Chapter 0: Quick Introduction to numpy, pandas, matplotlib
- Chapter 1: General Introduction to Machine Learning
- Chapter 2: Introduction to Classification
- Chapter 3: Overfitting and Cross Validation
- Chapter 4: Measuring Quality of a Classifier
---
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# Day 1
- Chapter 0: Quick Introduction to numpy, pandas, matplotlib
- Chapter 1: General Introduction to Machine Learning
- Chapter 2: Introduction to Classification
- Chapter 3: Overfitting and Cross Validation
- Chapter 4: Measuring Quality of a Classifier
- Chapter 5: Classification Algorithms Overview
---
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# Day 2
- Chapter 6: Pipelines and Hyperparameter Optimisation
---
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# Day 2
- Chapter 6: Pipelines and Hyperparameter Optimisation
- Chapter 7: Introduction to Regression
---
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# Day 2
- Chapter 6: Pipelines and Hyperparameter Optimisation
- Chapter 7: Introduction to Regression
- Chapter 8: Introduction to Neural Networks
---
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# Quick poll
---
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# Who used ... before ?
- Jupyter Notebooks
---
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# Who used ... before ?
- Jupyter Notebooks
- Pandas
---
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# Who used ... before ?
- Jupyter Notebooks
- Pandas
- Matplotlib
---
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# Who used ... before ?
- Jupyter Notebooks
- Pandas
- Matplotlib
- seaborn
---
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# Who used ... before ?
- Jupyter Notebooks
- Pandas
- Matplotlib
- seaborn
- numpy
---
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# Questions ?
---
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# Get started
<p>1. Start Computer, choose `fedora` during startup
<br/>
<br/>
---
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# Get started
<p>1. Start Computer, choose `fedora` during startup
<br/>
<br/>
<p>2. Login with your `NETHZ` credentials
<br/>
<br/>
---
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# Get started
<p>1. Start Computer, choose `fedora` during startup
<br/>
<br/>
<p>2. Login with your `NETHZ` credentials
<br/>
<br/>
<p>3. Start Terminal (click `activity` top-left corner)
<br/>
<br/>
---
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# Get started
<p>1. Start Computer, choose `fedora` during startup
<br/>
<br/>
<p>2. Login with your `NETHZ` credentials
<br/>
<br/>
<p>3. Start Terminal (click `activity` top-left corner)
<br/>
<br/>
<p>4. Enter <br/><br/><div style="font-family: Ubuntu Mono; font-size: .8em;"><center>curl https://sis.id.ethz.ch/mlw/install.sh | bash</center></div>
<br/>
<br/>
---
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# Get started
<p>1. Start Computer, choose `fedora` during startup
<br/>
<br/>
<p>2. Login with your `NETHZ` credentials
<br/>
<br/>
<p>3. Start Terminal (click `activity` top-left corner)
<br/>
<br/>
<p>4. Enter <br/><br/><div style="font-family: Ubuntu Mono; font-size: .8em;"><center>curl https://sis.id.ethz.ch/mlw/install.sh | bash</center></div>
<br/>
<br/>
<p>5. Wait and press `ENTER` when asked
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