Course overview
The course provides a theoretical and practical material on models and methods used for text, image and video generation. Learners will delve into the specifics of deep learning models with the focus on conditional and unconditional image generation (GAN, CycleGAN, score-based models, etc.), as well as sequence generation (RNN, LSTM, transformers, etc.). By analyzing examples and implementing tasks, students will gain knowledge and practical skills on multimodal models, text, image, and video generation and evaluation.
Learning Outcomes
By the end of this course, you will be able to:
- LO1: Explain the concepts of deep learning models used for text, image, and video generation.
- LO2: Use and evaluate the models for text generation.
- LO3: Use and evaluate the models for image and video generation.
Course Creator
Kaunas University of Technology
Course title:
Management of Generative AI Transformation
Content:
Learning objectives:
Analyze and integrate generative AI-driven transformations in work practices, demonstrating the ability to manage the development, adoption, and scaling of generative AI projects within organizational contexts. Thus, a completing a course, a learner should be able to
LO1. Analyze the impact of generative AI on work practices, roles, and processes.
LO2. Identify key opportunities and barriers for adopting and scaling generative AI in organizations.
LO3. Identify the principles and processes of AI transformation.
LO4. Design and apply change management strategies for implementing generative AI projects.
LO5. Lead generative AI initiatives and transformation with attention to strategic potential, designing value-driven use cases, strategy and roadmaps.