“Advanced AI Algorithms: Concepts, Methods & Applications” is a two-day advanced course designed for participants who already understand the fundamentals of Artificial Intelligence and want to deepen their knowledge of modern AI algorithms, their underlying concepts and their practical applications.
Coming soon – Advanced AI Algorithms: Concepts, Methods & Applications
Training details
Location
Bucharest
Start Date
Target Audiance
Technical
Teaching language(s)
Romanian / English
Organizing institution
ICI Bucharest
Delivery mode
On-site
Level
Advanced
Format
Case Study Session, Hands-on session, Lecture, Workshop
Topics / Keywords
Advanced AI, Machine Learning, Deep Learning, Transformers, Generative AI, Reinforcement Learning, Optimization, Explainable AI, AI Applications
What You Will Learn
After attending this course, participants will be able to:
- Understand the principles and assumptions behind advanced Artificial Intelligence and Machine Learning algorithms.
- Compare major algorithmic approaches and select appropriate methods according to the characteristics of a problem and dataset.
- Understand advanced neural architectures, including deep neural networks, attention mechanisms and Transformer-based models.
- Explore modern methods used in Generative AI, representation learning and reinforcement learning and understand their practical applications.
- Evaluate AI models using appropriate performance metrics and recognize common issues such as overfitting, bias, data limitations and model uncertainty.
- Apply and experiment with selected advanced AI methods through guided practical exercises and interpret the resulting model behaviour and performance.
Agenda
| DAY 1 – ADVANCED AI CONCEPTS & METHODS | Understanding modern algorithms and architectures |
| 10:00 – 10:30 | Advanced AI landscape From classical Machine Learning to Deep Learning, foundation models and modern AI systems; key terminology and current algorithmic families. |
| 10:30 – 11:30 | Advanced Machine Learning methods Supervised and unsupervised learning beyond the basics, ensemble methods, feature representations, dimensionality reduction and algorithm-selection criteria. |
| 11:30 – 12:30 | Deep Learning architectures Neural network design principles, convolutional and recurrent approaches, representation learning, training considerations and common optimization challenges. |
| 12:30 – 13:00 | Break |
| 13:00 – 14:15 | Attention mechanisms and Transformers Core concepts behind attention, Transformer architecture, embeddings and the foundations of modern language and multimodal models. |
| 14:15 – 15:15 | Generative AI and modern model families Generative modelling concepts, Large Language Models, diffusion-based approaches and examples of applications across different domains. |
| 15:15 – 16:00 | Reinforcement learning and advanced decision methods Agents, environments, rewards and policies; introduction to reinforcement learning and examples of decision-making applications. |
| DAY 2 – METHODS IN PRACTICE | Applying, evaluating and interpreting advanced AI algorithms |
| 10:00 – 11:00 | From problem to algorithm Translate a real-world problem into an AI task, assess data characteristics and select an appropriate algorithmic approach. |
| 11:00 – 12:15 | Guided practical exercise: advanced model workflow Prepare data, configure a selected model, run an experiment and examine the main parameters that influence model behaviour. |
| 12:15 – 12:45 | Break |
| 12:45 – 14:00 | Model evaluation and comparison Use relevant performance metrics, compare alternative approaches and interpret results while considering overfitting, generalization and uncertainty. |
| 14:00 – 15:00 | Explainability, robustness and responsible AI Explore model interpretability, bias, robustness, security and human oversight, and understand their relevance in real-world deployment. |
| 15:00 – 16:00 | Applications, case studies and final exercise Analyse advanced AI applications across sectors, discuss computational and implementation considerations, complete a guided exercise, and conclude with Q&A. |
Course Description
“Advanced AI Algorithms: Concepts, Methods & Applications” is a two-day advanced course designed for participants who already understand the fundamentals of Artificial Intelligence and want to deepen their knowledge of modern AI algorithms, their underlying concepts and their practical applications.
Day 1 focuses on the concepts and methods behind advanced AI. Participants will examine the evolution from classical Machine Learning to Deep Learning and modern foundation models, with emphasis on neural architectures, attention and Transformers, Generative AI, reinforcement learning and algorithm selection. The sessions will connect theoretical concepts with practical examples and discuss when different approaches are appropriate.
Day 2 focuses on applications and guided experimentation. Participants will work with selected advanced AI methods, explore model configuration and evaluation, compare results, and examine practical considerations such as data quality, computational requirements, explainability, robustness, bias and responsible use. By the end of the course, participants will be able to better understand, compare and apply advanced AI algorithms in realistic use cases.