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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

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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


---
class: remark-slide-content-large


## 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, chose  `fedora` during startup
<br/>
<br/>

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# Get started

<p>1. Start Computer, chose  `fedora` during startup
<br/>
<br/>
<p>2. Login with your `NETHZ` credentials
<br/>
<br/>

---
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# Get started

<p>1. Start Computer, chose  `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, chose  `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/>

---
class: remark-slide-content-large

# Get started

<p>1. Start Computer, chose  `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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