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DON’T JUST FOLLOW THE TREND – APPLY AI IN PRACTICE

The recent wave of innovation in Artificial intelligence (AI) has enormous disruptive potential, but there is a decided shortage of professionals capable of harnessing the power of the latest modelling techniques and moving AI from the drawing board into real life.

This course aims to give practitioners a solid foundation in data modelling, as well as an in-depth understanding of the latest AI methods and solutions (especially Deep Learning). The course covers the theoretical foundations of statistical modelling, the detailed analysis of neural models – along with associated machine learning procedures – and includes a technical introduction to and practice in Python programming using the general “Data Science Stack” (Numpy, SciPy, Pandas, Scikit-Learn), as well as TensorFlow for Deep Learning. After successfully finishing the course, practitioners will not only be familiar with the state of the art in AI, they will also be capable of implementing the latest machine-learning models in practice.

The course is conducted by two trainers who are also available as points of contact throughout the on-campus study period, ensuring that you receive the highest possible standard of mentoring and guidance. Please note that the two trainers are specialists in different areas of expertise, meaning that each will contribute his or her own perspective to the learning process.

Degree

Certified Expert in Data Science and Artificial Intelligence (Frankfurt School)

Target Group

Programmers, data scientists and business analysts, as well as individuals with experience in programming and/or data analysis seeking an in-depth understanding of and practical exposure to the latest AI technologies (especially Deep Learning)

Learning Target

Participants

  • gain insight of the historic trends in artificial intelligence
  • gain structured understanding of the data analysis pipeline
  • get solid foundations in data modelling with statistical methods (non-neural as well as neural models)
  • understand and implement in depth deep neural network models
    • Deep feedforward neural networks
    • Convolutional neural networks
    • Long short-term memory networks
  • learn the principles and practice of model evaluation, regularization and hyper parameter optimization
  • get familiar with the problem domains of AI
    • Natural language processing
    • Visual recognition - image processing
    • Time series analysis

Requirements

Participants will need basic programming skills and knowledge; a basic knowledge of Python would be a plus

  • A data-modelling background (or strong affinity) is desirable
  • All participants should bring laptops with them to work on practical exercises during the course.

Duration

10 days

Programme Structure

The 10 days are divided into two blocks of 5 days which are divided by a weekend.

Methodology:
Interactive lecture, collaborative "lab work" in programming environment, graded assignments

Exam:
The course will take the form of interactive “lab” sessions during which participants will implement solutions (under guidance). Passing the automatically evaluated programming assignments is a necessary prerequisite for certification.

Price

Total fee: EUR 6,990 (10 days). This includes the IT infrastructure access fee, registration fee (EUR 100) and examination fee (EUR 450).

Course fees are exempt from VAT

Product Information & Registration

Programme

Week 1: DATA SCIENCE AND MODELLING FOUNDATIONS

  • Foundational definitions, historical overview
  • Task settings, tasks of AI models
  • Data science pipeline
  • Visualization, representation and embedding
  • Clustering methods, anomaly detection models, classification methods
  • Regression, metrics, measurements of models

Week 2: NEURAL NETWORKS AND DEEP LEARNING

  • Training and setup for neural networks
  • Neural network basics
  • Current neural architectures and their application
  • Memory networks, unsupervised learning with neural models, transfer learning
  • Peak into what else is there?
  • Advice on deployment of Machine Learning models

Start of programme

Lecturers

Prof. Dr. Florian Ellsaesser

Florian is Assistant Professor for International Entrepreneurship at the Frankfurt School of Finance & Management.

He completed his undergraduate degree in Economics and Philosophy in the UK. His PhD at Cambridge University was a comparison of three approaches to explanation in management research, focusing on causal inference and the construction of explanatory frameworks. He particularly looked at situations in which the data is limited, such as when an entirely new product, service or distribution channel is launched.

For one of his research papers Florian received the Oxford, Cambridge, Warwick best PhD paper award. During his PhD he also began to study Mathematics and has continued ever since.

After his PhD, Florian first worked as a strategy consultant and then as a project manager at McKinsey & Company. He then moved into the entrepreneurial world and was involved in founding a number of technology start-ups. He also published on causal inference and machine learning in the Academy of Management Review and the Strategic Management Journal.

Florian’s main research interest lies in decision making under uncertainty and the application of machine learning to managerial and organizational problems.

András Simonyi

András is a computational linguist and philosopher with a strong background in logic. As a developer and researcher he worked on the design and implementation of various natural language processing based intelligent systems at Applied Logic Laboratory (2006-2015), the Hungarian Academy of Science (2013-2017) and Analogy Co. (2015-2017). His main fields of interest are computational semantics and knowledge representation. Currently he works as a natural language processing developer.

Levente Szabados

Levente has a background in cognitive sciences and considerable experience in leading research and development teams as well as transforming research results into software applications. He has filled various roles at two startups including co-founder, lead of research, and chief technology officer. Levente’s work has covered topics including web ontologies, evolutionary computation, neural networks, and diverse natural language processing approaches. He currently has over 10 years of experience in applied AI and is working as a Senior Consultant. 

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