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M.Sc.|MBA|Micro-Degree BDAI_deeplearning
Deep Learning (ML II)

Modern AI systems are powered by deep learning and advanced machine learning techniques. In this course, students will learn to build deep neural networks—with and without attention mechanisms—train models using popular AI libraries, and deploy them in the cloud. The course also introduces reinforcement learning, probabilistic graphical models, and recursive networks, giving students a strong foundation in both current and alternative AI approaches. Students will apply their skills in a final project to implement a complex AI application.

  1. Course Start:

  2. Course End:

  3. Estimated Effort:

    10 h/week
Book Micro-Degree Program Enrollment in this course is by invitation only*
  • *This course is subject to a fee. Please, book the course using the “Book Micro-Degree Program” button. If you have not registered at German-UDS.academy yet, please register with the same email address that you have used for the booking. If you already have an account at German-UDS.academy, please use the same email address for the booking system.

Deep Learning (ML II)

About This Course

In this module, the students learn how to in-terpret, modify and create new deep learning architectures. We look at the different kinds of networking, activation functions and shortcuts, developing a classification of architectural principles that can be used for new projects.

One important aspect is also understanding how to train networks and simplify them later, using self-learning principles.

Learning Objectives

  • Understand the pillars of sustainability and their effects on organizational behavior and performance
  • Understand business ethics and be able to apply ethical managerial practice in diverse contexts
  • Practice communication skills
  • Practice teamwork and problem solving
  • Are given the opportunity for self-assessment

Requirements

Students should have prior knowledge of basic machine learning concepts, proficiency in Python, and familiarity with foundational deep learning techniques. Experience with frameworks like TensorFlow or PyTorch is recommended but not required.

How much deep learning background is required?

Basic familiarity with neural networks and machine learning is expected. The course will build on these foundations toward more advanced topics like transformers and reinforcement learning.