Statistics for data science (Dan Nicolae)
- A data science pipeline
- Data exploration
- Statistical inference with resampling methods
Data science with Python (hands-on) (Dan Nicolae, Razvan Bunescu)
- Intro to Python
- Pandas and data frames
- Probability and simulations
Statistical learning (hands-on) (Dan Nicolae)
- Regression models and inference
- Prediction and classification
Intro to machine learning (Razvan Bunescu & ChatGPT)
- Feature vector representations
- ML for Classification
- ML for Regression
- Clustering
Machine learning (hands-on) (Razvan Bunescu & ChatGPT)
- ML algorithms in Python
- Implementation using NumPy
- The sklearn library
- Visualization using Matplotlib
- Experimental evaluation of ML models
- Linear vs. non-linear classification
Deep learning with neural networks (incl. hands-on) (Pawel Gasiorowski)
- Deep learning with Artificial Neural Networks
- Image Processing, Object Classification and Detection with Convolutional Neural Networks
- Implementation in Tensorflow Keras
- Regression and gradient descent
- Activation Functions, Feedforward Process, Error Functions, Optimizers, Backpropagation
- Logistic regression and NNs for non-linear classification
- Transfer Learning technique
Time-series analytics (Jože Rožanec)
- Time series analytics techniques: filtering methods, interpolation, extrapolation, prediction with ML
- Time series databases
- Python libraries for working with time-series data
Introduction to graph data (incl. hands-on) (Dumitru Roman, Brian Elvesæter, Radu Prodan)
- Introduction to graph data
- Knowledge Graphs
- NoSQL databases
- Graph databases (focus on Neo4j)
- Data model and data modeling
- Query language
- Graph algorithms / analytics / ML
- Introduction to massive graphs
Knowledge graphs for conversational AI (Ioan Toma)
- Introduction to Semantic Knowledge Graphs and their role in building intelligent chatbots
- Understanding knowledge modeling and ontology development for building Knowledge Graphs for Conversational AI
- Data import and mapping techniques to populate the Knowledge Graphs
- Overview of conversational setup and designing a chatbot interface
- Building a chatbot using Onlim Conversational AI framework
- Integration of Knowledge Graph data with the chatbot using API calls
- Querying and accessing Knowledge Graph data through Chatbots
FAIR data and best practices in data sharing (Anna Fensel)
- Introduction to FAIR data
- How to make data FAIR?
- Sharing data effectively with semantic technology:
- Open data vs. closed data
- Consent, contracts, licenses, legal compliance
- Research data infrastructures
Data enrichment (Dumitru Roman, Nikolay Nikolov)
- Data preparation; cleaning, annotating and enriching data
- Semantic data enrichment
- Tools for semantic enrichment
- Data enrichment pipelines
- Example application for data enrichment
Operationalizing machine learning pipelines (Wiktor Sowinski-Mydlarz)
- What are Machine Learning pipelines
- Introduction to Software Containers and Cloud
- Deployment, orchestration, monitoring of ML pipelines on the Cloud – using python libraries
- Applications example