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<img src="./front_page.png" width=100%/>
---
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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
- Almost 6 years old, almost 40 people.
- Zoo of experts with diverse background
---
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## About Scientific IT Services (SIS)
- IT Services close to research
- Almost 6 years old, almost 40 people.
- 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: 180%; color: #1F407A;">
We also offer data science support,
<br/>
so don't hesitate to
<br/>
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 our time planning works out.
---
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# About the course
* First version, might be a bit rough, not sure if our time planning works out.
* Pragmatic approach, little math.
---
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# About the course
* First version, might be a bit rough, not sure if our time planning works out.
* 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 about classical machine learning than about artificial neural networks.
---
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# About the course (2)
What will you learn?
* Basic concepts of Machine Learning (ML).
* More about 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 about 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/>
<center>
<div style="font-family: Roboto Mono; font-size: .8em; color: #0069B4;">
<center>curl https://sis.id.ethz.ch/mlw/install.sh | bash</center>
</div>
</center>
<p>5. Enter <br/><br/>
<center>
<div style="font-family: Roboto Mono; font-size: .8em; color: #0069B4;">
<center>./pycharm.sh</center>
</div>
</center>
<p>4. 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: Roboto Mono; font-size: .8em; color: #0069B4;"><center>curl https://sis.id.ethz.ch/mlw/install.sh | bash</center></div>
<br/>
<p>5. Wait and press `ENTER` when asked
---
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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: Roboto Mono; font-size: .8em; color: #0069B4;"><center>curl https://sis.id.ethz.ch/mlw/install.sh | bash</center></div>
<br/>
<p>5. Wait and press `ENTER` when asked.
<br/>
<br/>
<p>6. <strong>Don't switch of / shutdown the computer during the two days !</strong>
---
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## How to upload the notebooks to polybox
<p>1. Start Terminal (click `activity` top-left corner)
<br/>
<br/>
<p>2. Enter <br/><br/><div style="font-family: Roboto Mono; font-size: .8em; color: #0069B4;"><center>ln -s .tmp/ml_workshop $HOME</center></div>
<br/>
<p>3. You should find the notebooks in `ml_workshop` in your homefolder.
<br/>
.
<p>4. Open `https://polybox.ethz.ch` to upload your notebooks manually .
<br/>
---
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