class: inverse, center, middle, remark-frontslide-content <img src="./front_page.png" width=100%/> --- class: center, middle, inverse, remark-slide-content-large # About us --- class: remark-slide-content-large ## About Scientific IT Services (SIS) --- class: remark-slide-content-large ## About Scientific IT Services (SIS) - IT Services close to research - Almost 6 years old, almost 40 people. - Zoo of experts with diverse background --- class: remark-slide-content-large ## 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. Franziska Oschmann * Dr. Mikolaj Rybinski * Dr. Uwe Schmitt --- class: remark-slide-content-large ## Our Services - Scientific Computing Services: Euler, ... --- class: remark-slide-content-large ## Our Services - Scientific Computing Services: Euler, ... - Data Science Support --- class: remark-slide-content-large ## Our Services - Scientific Computing Services: Euler, ... - Data Science Support - Software Development: openBIS, ELN, ... --- class: remark-slide-content-large ## Our Services - Scientific Computing Services: Euler, ... - Data Science Support - Software Development: openBIS, ELN, ... - Scientific Visualization --- 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, ... --- 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 --- 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 --- 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 --- class: center, middle, remark-slide-content-large <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> --- class: center, middle, inverse, remark-slide-content-large # About the course --- class: remark-slide-content-large # About the course * Second iteration. --- class: remark-slide-content-large # About the course * Second iteration. * Pragmatic approach, little math. --- class: remark-slide-content-large # About the course * Second iteration. * Pragmatic approach, little math. * Introduction only! --- class: remark-slide-content-large # About the course (2) What will you learn? * Basic concepts of Machine Learning (ML). --- class: remark-slide-content-large # About the course (2) What will you learn? * Basic concepts of Machine Learning (ML). * We start with classical ML using `scikit-learn`. --- class: remark-slide-content-large # About the course (2) What will you learn? * Basic concepts of Machine Learning (ML). * We start with classical ML using `scikit-learn`. * General overview of supervised learning and related methods. --- class: remark-slide-content-large # About the course (2) What will you learn? * Basic concepts of Machine Learning (ML). * We start with classical ML using `scikit-learn`. * General overview of supervised learning and related methods. * Introduction to concepts of deep learning using `Keras`. --- class: remark-slide-content-large # About the course (3) What will you NOT learn? * How to program with Python. --- class: remark-slide-content-large # About the course (3) What will you NOT learn? * How to program with Python. * How exactly ML methods work. --- class: remark-slide-content-large # About the course (3) What will you NOT learn? * How to program with Python. * How exactly ML methods work. * Unsupervised learning methods. --- class: center, middle, inverse, remark-slide-content-large # Schedule --- class: remark-slide-content-large # Day 1 - Chapter 1: General Introduction to Machine Learning --- class: remark-slide-content-large # Day 1 - Chapter 1: General Introduction to Machine Learning - Chapter 2: Introduction to Classification --- class: remark-slide-content-large # Day 1 - Chapter 1: General Introduction to Machine Learning - Chapter 2: Introduction to Classification - Chapter 3: Overfitting and Cross Validation --- class: remark-slide-content-large # Day 1 - 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 --- class: remark-slide-content-large # Day 1 - 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: Pipelines and Hyperparameter Optimisation --- class: remark-slide-content-large # Day 1 - 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: Pipelines and Hyperparameter Optimisation - Chapter 6A: Classification Algorithms Overview --- class: remark-slide-content-large # Day 2 - Chapter 6B: Classification Algorithms Overview --- class: remark-slide-content-large # Day 2 - Chapter 6B: Classification Algorithms Overview - Chapter 7: Introduction to Regression --- class: remark-slide-content-large # Day 2 - Chapter 6B: Classification Algorithms Overview - Chapter 7: Introduction to Regression - Chapter 8: Introduction to Neural Networks --- class: remark-slide-content-large # Day 3 - Real world example: Using EEG data to predict hand movements --- class: remark-slide-content-large # Day 3 - Real world example: Using EEG data to predict hand movements - Real world example: Using Deep learning to detect tumor in images. --- class: remark-slide-content-large # Day 3 - Real world example: Using EEG data to predict hand movements - Real world example: Using Deep learning to detect tumor in images. - You work on your own data or on a data set we provided. --- class: remark-slide-content-large # About exercises - We have exercise sections during the script where you will work on your own. --- class: remark-slide-content-large # About exercises - We have exercise sections during the script where you will work on your own. - Don't hesitate to ask for assistance. --- class: remark-slide-content-large # About exercises - We have exercise sections during the script where you will work on your own. - Don't hesitate to ask for assistance. - We also have optional exercises if you get bored. --- class: center, middle, inverse, remark-slide-content-large # Quick poll --- class: remark-slide-content-large # Who used ... before? - Jupyter Notebooks --- class: remark-slide-content-large # Who used ... before? - Jupyter Notebooks - Pandas --- class: remark-slide-content-large # Who used ... before? - Jupyter Notebooks - Pandas - Matplotlib --- class: remark-slide-content-large # Who used ... before? - Jupyter Notebooks - Pandas - Matplotlib - seaborn --- class: remark-slide-content-large # Who used ... before? - Jupyter Notebooks - Pandas - Matplotlib - seaborn - numpy --- class: center, middle, inverse, remark-slide-content-large # Questions? --- class: remark-slide-content-large ## Get started Please follow **carefully** the instructions from the printout.