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updated intro presentation

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* Dr. Tarun Chadha
* Dr. Franziska Oschmann
* Dr. Mikolaj Rybinski
* Dr. Uwe Schmitt
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# About the course
* First version, might be a bit rough, not sure if our time planning works out.
* Second iteration.
---
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# About the course
* First version, might be a bit rough, not sure if our time planning works out.
* Second iteration.
* Pragmatic approach, little math.
......@@ -371,7 +371,7 @@ class: remark-slide-content-large
# About the course
* First version, might be a bit rough, not sure if our time planning works out.
* Second iteration.
* Pragmatic approach, little math.
......@@ -398,7 +398,7 @@ What will you learn?
* Basic concepts of Machine Learning (ML).
* More about classical machine learning than about artificial neural networks.
* We start with classical ML using `scikit-learn`.
---
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......@@ -410,12 +410,11 @@ What will you learn?
* Basic concepts of Machine Learning (ML).
* More about classical machine learning than about artificial neural networks.
* We start with classical ML using `scikit-learn`.
* General overview of supervised learning and related methods.
---
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......@@ -426,13 +425,14 @@ What will you learn?
* Basic concepts of Machine Learning (ML).
* More about classical machine learning than about artificial neural networks.
* We start with classical ML using `scikit-learn`.
* General overview of supervised learning and related methods.
* How to start with ML using `scikit-learn` and `Keras`.
* Introduction to concepts of deep learning using `Keras`.
---
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# Schedule
---
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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
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- 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
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- Chapter 4: Measuring Quality of a Classifier
- Chapter 5: Pipelines and Hyperparameter Optimisation
---
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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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- Chapter 5: Pipelines and Hyperparameter Optimisation
- Chapter 6A: Classification Algorithms Overview
---
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# Day 2
- Chapter 6: Classification Algorithms Overview
- Chapter 6B: Classification Algorithms Overview
---
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# Day 2
- Chapter 6: Classification Algorithms Overview
- Chapter 6B: Classification Algorithms Overview
- Chapter 7: Introduction to Regression
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# Day 2
- Chapter 6: Classification Algorithms Overview
- Chapter 6B: Classification Algorithms Overview
- Chapter 7: Introduction to Regression
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- Chapter 8: Introduction to Neural Networks
---
class: center, middle, inverse, remark-slide-content-large
# Quick poll
---
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# Who used ... before?
- Jupyter Notebooks
# Day 3
- Real world example: Using EEG data to predict hand movements
---
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# Who used ... before?
# Day 3
- Jupyter Notebooks
- Real world example: Using EEG data to predict hand movements
- Pandas
- Real world example: Using Deep learning to detect tumor in images.
---
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# Who used ... before?
# Day 3
- Jupyter Notebooks
- Real world example: Using EEG data to predict hand movements
- Pandas
- Real world example: Using Deep learning to detect tumor in images.
- Matplotlib
- You work on your own data or on a data set we provided.
---
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# Who used ... before?
- Jupyter Notebooks
# About exercises
- Pandas
- Matplotlib
- seaborn
- We have exercise sections during the script where you will work on your own.
---
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# Who used ... before?
# About exercises
- We have exercise sections during the script where you will work on your own.
- Jupyter Notebooks
- Don't hesitate to ask for assistance.
- Pandas
---
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# About exercises
- Matplotlib
- We have exercise sections during the script where you will work on your own.
- seaborn
- Don't hesitate to ask for assistance.
- numpy
- We also have optional exercises if you get bored.
---
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# Questions?
# Quick poll
---
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# Get started
<p>1. Start Computer, choose `fedora` during startup
<br/>
<br/>
---
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# Who used ... before?
# Get started
- Jupyter Notebooks
<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
# Who used ... before?
<p>1. Start Computer, choose `fedora` during startup
<br/>
<br/>
<p>2. Login with your `NETHZ` credentials
<br/>
<br/>
- Jupyter Notebooks
<p>3. Start Terminal (click `activity` top-left corner)
<br/>
<br/>
- Pandas
---
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# Get started
# Who used ... before?
<p>1. Start Computer, choose `fedora` during startup
<br/>
<br/>
<p>2. Login with your `NETHZ` credentials
<br/>
<br/>
- Jupyter Notebooks
<p>3. Start Terminal (click `activity` top-left corner)
<br/>
<br/>
- Pandas
<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>
- Matplotlib
---
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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/>
# Who used ... before?
<p>3. Start Terminal (click `activity` top-left corner)
<br/>
<br/>
- Jupyter Notebooks
<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/>
- Pandas
- Matplotlib
<p>5. Wait and press `ENTER` when asked
- seaborn
---
class: remark-slide-content-large
# Get started
# Who used ... before?
<p>1. Start Computer, choose `fedora` during startup
<br/>
<br/>
<p>2. Login with your `NETHZ` credentials
<br/>
<br/>
- Jupyter Notebooks
<p>3. Start Terminal (click `activity` top-left corner)
<br/>
<br/>
- Pandas
<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/>
- Matplotlib
<p>5. Wait and press `ENTER` when asked.
<br/>
<br/>
- seaborn
- numpy
---
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# Questions?
<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
## Get started
Please follow **carefully** the instructions from the printout.
<p>1. You should find the notebooks in `ml_workshop` in your homefolder.
<br/>
.
<p>2. Open `https://polybox.ethz.ch` to upload your notebooks manually.
<br/>
</textarea>
<script src="https://remarkjs.com/downloads/remark-latest.min.js">
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