Discover AI for High Tech

Duration 7 weeks
Weekly workload 8 to 12 hours
Course dates 9, 16, 23, 30 April and 21 May 2021
Tuition fee 2,950 VAT exempt
Location JADS Mariënburg / EAISI Gaslab /

Discover what AI in High Tech is really about in this 5-day program. 

The program combines a practical approach, working through example AI projects thereby showing how to successfully execute an AI project, with building a solid understanding of the fundamental principles underlying machine learning.

The teaching staff possesses a combination of scientific excellence and deep practical knowledge. Let them teach you about loading, exploring, and preparing data, about correlation and regression, supervised learning, nearest neighbor classification, linear classification, model complexity and decision boundaries, decision trees, random forests, support vector machines, under-fitting and overfitting, unsupervised learning, dimensionality reduction, principal components analysis, t-SNE, and much more.

discover

What you will do

You learn about the different types of artificial intelligence projects and whether or not a certain type of project is suitable for a machine learning approach. By working through the example projects you learn to recognize and deal with typical challenges and common pitfalls. The example projects cover the phases of business understanding, data understanding, data preparation, modeling and evaluation of the CRISP-DM model. It also teaches you to critically think about what to achieve with an AI project, how to capture that in a performance metric and how to assess whether your models generate value to your organisation. Note that Python is used as the primary language of instruction, but our teaching staff also supports R.

The Program day 1 & 2

Day 1: Artificial Intelligence and AI Project Execution

Theory

  • Introduction to Artificial Intelligence
  • Types of problems and technologies
  • Introduction to Machine Learning
  • Project Execution (CRISP-DM)
  • Scoping your project (Business Understanding)
  • Aims of Data Understanding

Practical

  • Getting started with Jupyter
  • Describing and visualizing data
  • Descriptive analytics
  • Going from Data Understanding back to Business Understanding (CRISP-DM iteration)

Day 2: Data Preparation

Theory

  • Data Preparation
  • Feature selection and feature engineering
  • Resampling methods

Practical

  • Selecting, cleaning and constructing data
  • Feature selection and feature engineering
  • Cross-validation

The Program day 3 till 5

Day 3: Modeling: Supervised Learning

Theory

  • Basic supervised learning algorithms
  • Regression and classification
  • Performance metrics, also in relation to business understanding
  • Evaluation / interpretation of results (does it meet business objectives)

Practical

  • Regression models (linear regression, LASSO, KNN)
  • Classification models (SVM, CART, Random Forests, Gradient Boosting)
  • Visualization and tuning
  • Describing results

Day 4: Modeling: Unsupervised Learning

Theory

  • Basic unsupervised learning algorithms
  • Clustering (k-means, hierarchical clustering)
  • Dimensionality reduction (PCA, t-SNE)

Practical

  • Hands-on exercises with clustering  and dimensionality reduction
  • Underfitting and overfitting

Day 5: Recap, reflection, and best practices

Theory

  • Recap of all theory covered in course
  • Presenting results of the Discover projects
  • Overview of best AI practices within organizations (infrastructure, deployment)
  • Wrap-up

Practical

  • Q&A on Python for AI
JADS Marienburg campus

Your profile

  • You want to learn what AI can mean for your career and organization;
  • You want to dive deep into a specific topic;
  • The prospect of learning to program in Python appeals to you;
  • You work in a technical role and/or a managerial role;
  • You have  a BSc or MSc and at least 3 years of working experience.

You walk away with

After completing the 5-day program, participants:

  • Have an overview of the fields of Artificial Intelligence and Machine Learning;
  • Understand the type of problems one can solve with which type of AI techniques;
  • Can execute an AI project using the CRISP-DM methodology;
  • Understand the basic principles underlying commonly used AI techniques;
  • Know how to apply those AI techniques in Python and/or R and integrate them in a pipeline to load, explore, prepare and model data, and evaluate results.

Teaching in Covid times …

We’ve created a welcoming and warm online teaching environment, which we use as long as the covid situation prevents classroom teaching. Instructors and teaching technologies are adjusted to online teaching. We miss the friendly and inspirational atmosphere of our historic campus, but the teaching effectiveness of online course matches that of classroom teaching!

Our professors

Jerry de Groot

Jerry’s interest for technology already showed in his childhood hobby of “repairing” broken devices. Specializing in biomedical engineering, Jerry conducted several clinical trials for his PhD on improving mammography at the AMC. He stayed on at the spin-off company Sigmascreening to develop a better mammography product, for which he taught himself modern tools for rapid prototyping such as 3D-printing and automation with RaspberryPi, as well as various machine learning techniques in Python.

Daniel Kapitan

Daniel Kapitan is a Data Science expert with over twenty years of work experience. His personal motivation is to make the “language” of data accessible to a wider audience. He is optimistic about the potential of machine learning for social issues, but at the same time sees the concerns that exist in society with regard to the safe, transparent and responsible use of data and machine learning.

Any questions? Get in touch.

Application form

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