Schedule

Detailed information about the activities

Statistics for data science (Dan Nicolae)

• Foundations of data analysis
• Statistical inference with resampling methods
• Probability and simulations

Machine learning (Dan Nicolae)

• Linear models and inference
• Model complexity
• Prediction and classification
• Neural networks

Large Language Models (LLMs) – reasoning capabilities and model calibration (Cornelia Caragea)

• Prompting strategies in LLMs – Zero-Shot vs. In-Context Learning
• LLMs reasoning capabilities
• LLMs calibration – do they know what they do not know?

Deep learning with neural networks (incl. hands-on) (Gabriel Terejanu)

• Representation learning
• Deep learning with neural networks
• Implementation in PyTorch
  • Regression and gradient descent
  • Logistic regression and NNs for non-linear classification
  • RNNs for time series prediction

Causal inference in machine learning (incl. hands-on) (Gabriel Terejanu)

• Why do we need causality in data science?
• What is a causal model?
• What is an intervention?
• How to estimate causal effects?
• How to learn a causal model?

Knowledge graphs (Dumitru Roman and Roberto Avogadro)

• Intro to graph data structure
• Knowledge Graphs
• Graph data management (graph databases with Noe4j, graph data model, graph construction and querying)

LLMs and Agentic AI (Ioan Toma)

• Introduction to Agentic AI
• Agent Frameworks

Conversational AI (Ioan Toma)

• Conversational AI setup and designing a chatbot interface
• Semantic Knowledge Graphs and their role in Conversational AI
• Building a chatbot using Onlim Conversational AI framework

Edge Federated Learning (Radu Prodan)

• Parallel computing architectures
• Multiprocessing
• Parallel algorithms
• Parallel computing for AI and data science

Data/AI pipelines (Nikolay Nikolov)

• Introduction to data/AI pipelines
• Data/AI pipelines using containers

Operationalizing data and AI pipelines (Wiktor Sowinski-Mydlarz)

• Contemporary data processing
• GATE Institute Data Platform
• Alternatives and decisions
• Pipeline lifecycle

Management of data and AI pipelines (Wiktor Sowinski-Mydlarz)

• Deployment of data and ML pipelines
• Orchestration of data and ML pipelines
• Monitoring of data and ML pipelines