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The recent wave of innovation in Artificial intelligence (AI) has enormous disruptive potential, but there is a definite shortage of professionals capable of harnessing the power of the newest modeling techniques and bringing AI from the drawing board to real life practice.

This course is aiming to give practitioners a solid foundation in data modeling as well as an in depth understanding of recent AI methods and solutions (especially Deep Learning).

The course covers the theoretical foundations of statistical modelling, the detailed analysis of neural models and the associated machine learning procedures, and includes technical introduction and programming practice utilising the general (Python) “Data Science Stack” (Numpy, SciPy, Pandas , Scikit-Learn), as well as TensorFlow for Deep Learning.

The course will be conducted by two trainers who will also be available as points of contact throughout the seminar time. In this way, we ensure a high level of mentoring and guidance for the participants. It is also worth highlighting that the two trainers come from different areas of expertise and bring with them differing perspectives.

Degree

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

Target Group

Programmers, Data Scientist, Business Analysts, people with experience in programming and/or data analysis aiming to understand in depth and learn in practice the current technologies in Artificial Intelligence (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

  • Basic knowledge and capability in programming is necessary, basic knowledge of Python is a plus
  • Data modelling background or strong affinity desirable
  • Each participant must bring a laptop to work on practical exercises during the courses

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 entails interactive "lab" sessions where participants are implementing solutions (under guidance). Passing the automatically evaluated programming assignments is necessary precondition for certification.

Price

Total fee: 8,200 Euro. This includes the registration (100 Euro), and exam (450 Euro). All these amounts 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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