## Module 2

• Tool used: zoom
• Students that have questions may "raise" their hands
• Students write their questions in the chat area
• I repeat the question without identifying the student and try to answer
• Classes will be recorded.
• Most material will be available in this webpage
• Every week there will be a worksheet about a paper proposed in the previous week

Topics to be chosen for presentation plus instructions

What to hand in?

Classes

April 2nd

Knowledge Representation (PT) (EN) (Recorded Class (PT))
Exercises (Solutions)
Summary: Knowledge representation. Data x Knowledge. Various ways of representing data and knowledge: natural language, databases, programming languages and data structures, scripts, frames, semantic networks, knowledge graphs, logic
Relational inductive biases, deep learning, and graph networks

April 9th: Easter Holiday

April 16th

Probabilistic Logic Programming (Recorded Class)
Practical
Summary: ~1h to answer worksheet #1. Discussion. Limitations of graph representations. Probabilistic Logic Programming. ProbLog. Worlds. Inference. Basic examples of ProbLog.
Inference and learning in probabilistic logic programs using weighted boolean formulas

April 23rd

Relational Machine Learning (up to slide 13 + 25-35) (aleph run) (Recorded Class)
Summary: ~30 minutes to have a look at the worksheet and head start. Discussion about questions posed by students by email (mostly about section 3 of GraphNets paper). Introduction to Inductive Logic Programming (ILP).
Inductive Logic Programming
ILP turns 20
Turning 30: New Ideas in Inductive Logic Programming

April 30th

Relational Machine Learning (cont.) (Recorded Class)
Practical: learning first order rules with Aleph
Summary: ~30 minutes to have a look at the worksheet and head start. Relational Learning using Aleph. Practical: learning parent and grandparent.
Neural Networks for Relational Data

May 7th

Relational Machine Learning (cont.) (Recorded Class)
Markov Models
Markov Networks
Summary: ~30 minutes to have a look at the worksheet and head start. Learning rules: using refinement operators and controlling the search space. Markov chain and Markov networks.
Scalable Neural Methods for reasoning with a symbolic knowledge base

May 14th