EDSA BOOTCAMP LONDON - MACHINE LEARNING FOR DATA SCIENTISTS
The Open Data Institute
65 Clifton St
Many businesses are already using deep machine learning to make predictions. We can already see how machine learning gives rise to new intelligent applications, from self-driving cars to intelligent assistants on our smartphones. As a new breed of software that is able to learn without being explicitly programmed, machine learning (and deep learning) can access, analyze, and find patterns in Big Data in a way that is beyond human capabilities. The business advantages are huge, and the market is expected to be worth $47 billion by 2020
European Data Science Academy (EDSA) in collaboration with its partners is excited to announce a series of Summer Bootcamps across Europe to get you started on your machine learning journey - helping you get hands-on experience with machine learning models, methods, and algorithms. With the knowledge and skills gained from this workshop, you will be able to tackle real-world machine learning challenges.
EDSA Machine Learning Bootcamp for Data Scientists will take you through the conceptual and applied foundations of the subject. Topics covered will include machine learning theory, types of learning, techniques, models and methods. Labs are developed to practically learn how to use the R programming language and packages for applying the main concepts and techniques of Machine Learning.
So, be part of the AI revolution by attending this Bootcamp to learn the fundamentals of machine learning and leave armed with practical skills to extract value and predictions from data. This Bootcamp provides a hands-on introduction to the exciting, high-demand field of machine learning. With the knowledge and skills gained from this workshop, you will be able to tackle real-world machine learning challenges.
TOP REASONS TO ATTEND
Define machine learning, why it matters, and discuss its relationship to analytics, data science, and big data.
Machine learning fundamentals, the importance of algorithms, and machine learning as a service.
Basics of R platform, programming language concepts, common and useful R commands, and applying machine learning methods.
Understanding the steps in the machine learning pipeline, from data acquisition and feature generation, to training and model selection.
EDSA STUDY GUIDE: THE ESSENTIALS OF DATA ANALYTICS AND MACHINE LEARNING
As a participant in this Bootcamp, you will receive an exclusive
copy of this study guide. The guide provides both a deep understanding
of the techniques and practices of machine learning and exposes a wide
set of resources capable of being wielded by the data scientist and
analysts in their work. Readers will encounter explanations of the
theory behind the algorithms and models they are exposed to, giving them
an understanding of the strengths and weaknesses of each which they
should be able to use to reason about suitable approaches to real life
problem – and to communicate such reasoning to other stakeholders in
WHO SHOULD ATTEND THIS BOOTCAMP?
Have you tried learning data science and machine leaning by reading books or taking online courses, but have been discouraged? If so, this is the Bootcamp for you.
Bootcamp is aimed at business and technology professionals, developers, architects, data analysts, IT engineers, technology managers, and all those who wish to learn applied machine learning. This is also a great opportunity for recent university graduates who would like to explore data science as a career possibility.
Prerequisite: undergraduate-level linear algebra and statistics; basic R programming experience.
09:00 AM TO 09:30 AM REGISTRATION AND WELCOME COFFEE
09:30 AM TO 10:30 AM DATA SCIENCE AND MACHINE LEARNING IN THE ENTERPRISE
What is machine learning, why it
matters, examine what data driven intelligent era means and why analyzing data is going to increasingly form part of everyone's work.
10:30 AM TO 10:45 AM COFFEE BREAK
10:45 AM TO 11:00 AM MACHINE LEARNING CONCEPTS AND WORKFLOW
Discuss content of course and how it fits into data science and machine learning. Examine the workflow of a typical data science project and role of data scientist. See where the topics we cover appear in the workflow.
11:00 AM TO 12:30 PM REGRESSION MODELS
Learn how to model regression problems with linear regression, Poisson regression, polynomial regression and neural networks. Understand the concept of feature transformation and the mechanisms of regression neural networks.
12:30 PM TO 01:00 PM EVALUATION AND SELECTION OF REGRESSION MODELS
Learn how to evaluate regression models and select the best via validation and cross-validation techniques. Examine how we can judge data divisions necessary for validation techniques with learning graphs and statistical hypothesis tests. Learn how to select modelling algorithm hyper-parameters.
01:00 PM TO 02:00 PM LUNCH BREAK
02:00 PM TO 03:00 PM EVALUATION AND SELECTION OF REGRESSION MODELS (cont.)
03:00 PM TO 03:30 PM COMPLEXITY AND REGULARIZATION
Understand the relationships between model complexity, data size and performance. Learn how to manage complexity via subset selection and regularization. Learn lasso and ridge regression. Learn to regularize neural networks through weight decay and early stopping.
03:30 PM TO 03:45 PM COFFEE BREAK
03:45 PM TO 04:30 PM COMPLEXITY AND REGULARIZATION (cont.)
04:30 PM TO 05:30 PM PREPROCESSING I: FEATURE SELECTION & TRANSFORMATION
Learn and apply pairwise statistical testing techniques for selecting high-information features.
05:30PM DAY 1 CLOSING REMARKS
09:00 AM TO 09:30 AM COFFEE AND NETWORKING
09:30 AM TO 10:30 AM PREPROCESSING II: FEATURE TRANSFORMATION
Understand and learn to apply feature transformations, such as principle component analysis. Understand the relationship between such techniques and feature selection.
10:30 AM TO 10:45 AM COFFEE BREAK
10:45 AM TO 12:00 PM CLASSIFICATION MODELS
Learn how to model classification problems with LDA, QDA, logistic regression, support vector machines and neural networks. Understand mechanisms of support vector machines and classification neural networks.
12:00 PM TO 01:00 PM EVALUATION AND SELECTION OF CLASSIFICATION MODELS
Learn how to evaluation classification models, including in cases of imbalanced data and unequal error cost. Learn statistical hypothesis tests suitable for evaluating data divisions with classification models.
01:00 PM TO 02:00 PM LUNCH BREAK
02:00 PM TO 03:00 PM ENSEMBLE MODELS
Learn to use decision-tree ensemble models such as random forests and adaboost.
03:00 PM TO 03:15 PM COFFEE BREAK
03:15 PM TO 05:15 PM CAPSTONE PROJECT
In small groups you will apply your new skills to a pocket data science project.
05:15 PM TO 05:30 PM RECAP AND CLOSING REMARKS
BOOTCAMP ATTENDEE INSTRUCTIONS
You must bring your own laptop to this Bootcamp. It is essential that you have downloaded and installed both R and RStudio. They are available at:
R -https://www.r-project.org/RStudio - https://www.rstudio.com/
Note that these are two separate programs: R is a programming language, and RStudio is an IDE for the programming language. Once they are both installed, run RStudio and make sure that it works.
If you are unfamiliar with R, it will be helpful for you to take some online tutorials to familiarize yourself with the syntax. One such tutorial is available at: Introduction to R tutorial http://tryr.codeschool.com/
VENUE : Open Data Institute, 3rd Floor, 65 Clifton Street, London, EC2A 4JE. Tel: 020 3598 9395
DR. MIKE ASHCROFT - SENIOR DATA SCIENCE RESEARCHER & MACHINE LEARNING EXPERT
Dr. Mike Ashcroft is a senior researcher and data scientist. He is an experienced machine learning practitioner, with particular interests in graphical models for causal diagnostics, predictive analytics and system control. He completed his doctorate at the University of Melbourne, and his post-doc with Fudan University in Shanghai and the Australian Government. He lectures at Uppsala University in Sweden. He is a member in the European Network for Industrial and Business Statistics (ENBIS) and the Swedish Artificial Intelligence Society (SAIS). He also has experience in software development for data science tools, including remote, distributed and high-performance real-time systems.
ALI SYED - CHIEF SCIENTIST AND ENTERPRISE INTELLIGENCE EXPERT
As a global data and AI thought leader, Ali is collaborating with thinkers, researchers, designers, makers, doers, and business leaders. He has more than 16 years of professional experience and success assisting public and commercial organisations in using data analytics, insights and machine intelligence as a value amplifier. He works with people to understand and translate their aspirations into data and analytical solutions that enhance their ability to make choices, better decisions, realize performance gains and uncover opportunities. Before founding Persontyle, Ali worked with some of the leading technology and consulting organisations of the world namely PwC, KPMG BearingPoint, Sapient, EMC, UBS, NHS UK, and Capgemini.
Register online here or you can also reserve your seat by emailing on email@example.com
For group discounts and corporate bookings please email your requirements to firstname.lastname@example.org
ABOUT EUROPEAN DATA SCIENCE ACADEMY (EDSA)
The ‘Age of Data’ continues to thrive, with data being produced from all industries at a phenomenal rate that introduces numerous challenges regarding the collection, storage and analysis of this data. To address these challenges, the European Data Science Academy (EDSA):
1. Analyses sector specific skill sets for data analysts across Europe’s main industrial sectors;
2. Develops modular and adaptable curricula to meet these Data Science needs; and
3. Delivers training supported by multi-platform and multilingual learning resources based on these curricula.
The EDSA’s curricula and learning resources are guided and evaluated by experts in both Data Science and pedagogy to ensure they meet the needs of the Data Science community. To learn more about the EDSA, our Skills Dashboard and Courses Portal, and to become a member please visit http://edsa-project.eu/
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 643937
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