Statistics for Data ScienceStatistical learning (hands-on)Machine Learning (hands-on)Time Series (incl. hands-on)Knowledge Graphs for conversational AI (incl. (hands-on)FAIR Data and best practices in data sharingOperationalizing Machine Learning pipelinesDepartures
ArrivalsLunch break and socializing activities
Data Science with Python (hands-on)Intro to Machine LearningDeep Learning with Neural Networks (incl. hands-on)Introduction to Graph Data (incl. hands-on)Social EventData enrichment (incl. hands-on)Operationalizing Machine Learning pipelines (hands-on)
Intro eventFree timeFree time

Detailed information about the activities

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

Required software

Software tools/services to be used during the sessions and hands-on include: