#kdai2026 — Public Fediverse posts

🤠 #KDAI2026 final lecture on #KnowledgeGraphs 04 & #NeurosymbolicAI

The final graph session, then the leap to hybrid AI:

▸ OWL — complex classes & property restrictions
▸ SHACL Shapes
▸ Neurosymbolic AI with KG embeddings, RAG,
▸ AI limits revisited (ELIZA, Clever Hans, Chinese Room, Turing Test, the paperclip maximiser & the singularity).

Neither symbols nor learning alone get us there. 📡 SEE YOU SPACE COWBOY…

#SemanticWeb #OWL #SHACL #RAG #Ontologies #LLM @fiz_karlsruhe @KIT_Karlsruhe #AI

🤠 #KDAI2026 final lecture on #KnowledgeGraphs 04 & #NeurosymbolicAI

The final graph session, then the leap to hybrid AI:

▸ OWL — complex classes & property restrictions
▸ SHACL Shapes
▸ Neurosymbolic AI with KG embeddings, RAG,
▸ AI limits revisited (ELIZA, Clever Hans, Chinese Room, Turing Test, the paperclip maximiser & the singularity).

Neither symbols nor learning alone get us there. 📡 SEE YOU SPACE COWBOY…

#SemanticWeb #OWL #SHACL #RAG #Ontologies #LLM @fiz_karlsruhe @KIT_Karlsruhe #AI

🤠 #KDAI2026 final lecture on #KnowledgeGraphs 04 & #NeurosymbolicAI

The final graph session, then the leap to hybrid AI:

▸ OWL — complex classes & property restrictions
▸ SHACL Shapes
▸ Neurosymbolic AI with KG embeddings, RAG,
▸ AI limits revisited (ELIZA, Clever Hans, Chinese Room, Turing Test, the paperclip maximiser & the singularity).

Neither symbols nor learning alone get us there. 📡 SEE YOU SPACE COWBOY…

#SemanticWeb #OWL #SHACL #RAG #Ontologies #LLM @fiz_karlsruhe @KIT_Karlsruhe #AI

🤠 #KDAI2026 final lecture on #KnowledgeGraphs 04 & #NeurosymbolicAI

The final graph session, then the leap to hybrid AI:

▸ OWL — complex classes & property restrictions
▸ SHACL Shapes
▸ Neurosymbolic AI with KG embeddings, RAG,
▸ AI limits revisited (ELIZA, Clever Hans, Chinese Room, Turing Test, the paperclip maximiser & the singularity).

Neither symbols nor learning alone get us there. 📡 SEE YOU SPACE COWBOY…

#SemanticWeb #OWL #SHACL #RAG #Ontologies #LLM @fiz_karlsruhe @KIT_Karlsruhe #AI

🤠 #KDAI2026 final lecture on #KnowledgeGraphs 04 & #NeurosymbolicAI

The final graph session, then the leap to hybrid AI:

▸ OWL — complex classes & property restrictions
▸ SHACL Shapes
▸ Neurosymbolic AI with KG embeddings, RAG,
▸ AI limits revisited (ELIZA, Clever Hans, Chinese Room, Turing Test, the paperclip maximiser & the singularity).

Neither symbols nor learning alone get us there. 📡 SEE YOU SPACE COWBOY…

#SemanticWeb #OWL #SHACL #RAG #Ontologies #LLM @fiz_karlsruhe @KIT_Karlsruhe #AI

#KDAI2026 lecture no 11. This time it's interrogation tactics: FILTER, REGEX, OPTIONAL, UNION, negation, BIND, and GROUP BY aggregates in SPARQL.

Then a bounty comparison: DBpedia (1.32B triples, built from Wikipedia infoboxes) vs Wikidata (17.6B triples, ~29K editors)

Closing out with OWL: Description Logic SROIQ(D), etc.

See you space cowboy…

#SemanticWeb #KnowledgeGraphs #SPARQL #DBpedia #Wikidata #OWL #LinkedData #AIeducation @fiz_karlsruhe @KIT_Karlsruhe @fizise #AI #cowboybebop

#KDAI2026 lecture no 11. This time it's interrogation tactics: FILTER, REGEX, OPTIONAL, UNION, negation, BIND, and GROUP BY aggregates in SPARQL.

Then a bounty comparison: DBpedia (1.32B triples, built from Wikipedia infoboxes) vs Wikidata (17.6B triples, ~29K editors)

Closing out with OWL: Description Logic SROIQ(D), etc.

See you space cowboy…

#SemanticWeb #KnowledgeGraphs #SPARQL #DBpedia #Wikidata #OWL #LinkedData #AIeducation @fiz_karlsruhe @KIT_Karlsruhe @fizise #AI #cowboybebop

#KDAI2026 lecture no 11. This time it's interrogation tactics: FILTER, REGEX, OPTIONAL, UNION, negation, BIND, and GROUP BY aggregates in SPARQL.

Then a bounty comparison: DBpedia (1.32B triples, built from Wikipedia infoboxes) vs Wikidata (17.6B triples, ~29K editors)

Closing out with OWL: Description Logic SROIQ(D), etc.

See you space cowboy…

#SemanticWeb #KnowledgeGraphs #SPARQL #DBpedia #Wikidata #OWL #LinkedData #AIeducation @fiz_karlsruhe @KIT_Karlsruhe @fizise #AI #cowboybebop

#KDAI2026 lecture no 11. This time it's interrogation tactics: FILTER, REGEX, OPTIONAL, UNION, negation, BIND, and GROUP BY aggregates in SPARQL.

Then a bounty comparison: DBpedia (1.32B triples, built from Wikipedia infoboxes) vs Wikidata (17.6B triples, ~29K editors)

Closing out with OWL: Description Logic SROIQ(D), etc.

See you space cowboy…

#SemanticWeb #KnowledgeGraphs #SPARQL #DBpedia #Wikidata #OWL #LinkedData #AIeducation @fiz_karlsruhe @KIT_Karlsruhe @fizise #AI #cowboybebop

#KDAI2026 lecture no 11. This time it's interrogation tactics: FILTER, REGEX, OPTIONAL, UNION, negation, BIND, and GROUP BY aggregates in SPARQL.

Then a bounty comparison: DBpedia (1.32B triples, built from Wikipedia infoboxes) vs Wikidata (17.6B triples, ~29K editors)

Closing out with OWL: Description Logic SROIQ(D), etc.

See you space cowboy…

#SemanticWeb #KnowledgeGraphs #SPARQL #DBpedia #Wikidata #OWL #LinkedData #AIeducation @fiz_karlsruhe @KIT_Karlsruhe @fizise #AI #cowboybebop

From raw statements to sound inference. This week's #KDAI2026 session works through the RDF stack top to bottom:
▸RDF — typed literals, blank nodes, and Turtle
▸RDFS — classes, domains & ranges, subClassOf hierarchies, model-theoretic semantics, reification & RDF*
▸SPARQL — first steps

The triple is the message. 📡

@fiz_karlsruhe @fizise @KIT_Karlsruhe #semanticweb #RDF #KnowledgeGraphs #LinkedData #AI #lecture #sparql

From raw statements to sound inference. This week's #KDAI2026 session works through the RDF stack top to bottom:
▸RDF — typed literals, blank nodes, and Turtle
▸RDFS — classes, domains & ranges, subClassOf hierarchies, model-theoretic semantics, reification & RDF*
▸SPARQL — first steps

The triple is the message. 📡

@fiz_karlsruhe @fizise @KIT_Karlsruhe #semanticweb #RDF #KnowledgeGraphs #LinkedData #AI #lecture #sparql

From raw statements to sound inference. This week's #KDAI2026 session works through the RDF stack top to bottom:
▸RDF — typed literals, blank nodes, and Turtle
▸RDFS — classes, domains & ranges, subClassOf hierarchies, model-theoretic semantics, reification & RDF*
▸SPARQL — first steps

The triple is the message. 📡

@fiz_karlsruhe @fizise @KIT_Karlsruhe #semanticweb #RDF #KnowledgeGraphs #LinkedData #AI #lecture #sparql

From raw statements to sound inference. This week's #KDAI2026 session works through the RDF stack top to bottom:
▸RDF — typed literals, blank nodes, and Turtle
▸RDFS — classes, domains & ranges, subClassOf hierarchies, model-theoretic semantics, reification & RDF*
▸SPARQL — first steps

The triple is the message. 📡

@fiz_karlsruhe @fizise @KIT_Karlsruhe #semanticweb #RDF #KnowledgeGraphs #LinkedData #AI #lecture #sparql

From raw statements to sound inference. This week's #KDAI2026 session works through the RDF stack top to bottom:
▸RDF — typed literals, blank nodes, and Turtle
▸RDFS — classes, domains & ranges, subClassOf hierarchies, model-theoretic semantics, reification & RDF*
▸SPARQL — first steps

The triple is the message. 📡

@fiz_karlsruhe @fizise @KIT_Karlsruhe #semanticweb #RDF #KnowledgeGraphs #LinkedData #AI #lecture #sparql

The new #KDAI2026 lecture is out. How to get from a sentence to something a machine can actually reason?

The greenhouse effect was discovered by Fourier, explained by Eunice Newton Foote. 3 words, 1 triple: subject, predicate, object. Stack enough of them and you get a graph. Add an ontology and the machine starts to infer.

This session:
▸ Graphs & triples
▸ #KnowledgeGraphs & #ontologies
▸ #SemanticWeb & #LinkedData
▸ #RDF, RDFS, #SPARQL, #OWL, #SHACL
CU space cowboy…

@fiz_karlsruhe @fizise

The new #KDAI2026 lecture is out. How to get from a sentence to something a machine can actually reason?

The greenhouse effect was discovered by Fourier, explained by Eunice Newton Foote. 3 words, 1 triple: subject, predicate, object. Stack enough of them and you get a graph. Add an ontology and the machine starts to infer.

This session:
▸ Graphs & triples
▸ #KnowledgeGraphs & #ontologies
▸ #SemanticWeb & #LinkedData
▸ #RDF, RDFS, #SPARQL, #OWL, #SHACL
CU space cowboy…

@fiz_karlsruhe @fizise

The new #KDAI2026 lecture is out. How to get from a sentence to something a machine can actually reason?

The greenhouse effect was discovered by Fourier, explained by Eunice Newton Foote. 3 words, 1 triple: subject, predicate, object. Stack enough of them and you get a graph. Add an ontology and the machine starts to infer.

This session:
▸ Graphs & triples
▸ #KnowledgeGraphs & #ontologies
▸ #SemanticWeb & #LinkedData
▸ #RDF, RDFS, #SPARQL, #OWL, #SHACL
CU space cowboy…

@fiz_karlsruhe @fizise

The new #KDAI2026 lecture is out. How to get from a sentence to something a machine can actually reason?

The greenhouse effect was discovered by Fourier, explained by Eunice Newton Foote. 3 words, 1 triple: subject, predicate, object. Stack enough of them and you get a graph. Add an ontology and the machine starts to infer.

This session:
▸ Graphs & triples
▸ #KnowledgeGraphs & #ontologies
▸ #SemanticWeb & #LinkedData
▸ #RDF, RDFS, #SPARQL, #OWL, #SHACL
CU space cowboy…

@fiz_karlsruhe @fizise

The new #KDAI2026 lecture is out. How to get from a sentence to something a machine can actually reason?

The greenhouse effect was discovered by Fourier, explained by Eunice Newton Foote. 3 words, 1 triple: subject, predicate, object. Stack enough of them and you get a graph. Add an ontology and the machine starts to infer.

This session:
▸ Graphs & triples
▸ #KnowledgeGraphs & #ontologies
▸ #SemanticWeb & #LinkedData
▸ #RDF, RDFS, #SPARQL, #OWL, #SHACL
CU space cowboy…

@fiz_karlsruhe @fizise

This week, session 08 of #KDAI2026 lecture 08: NLP 04 went live.
From words to vectors, from vectors to meaning:
- 🔤 TF-IDF & sparse document vectors
- 🎲 Naive Bayes classification (spam, sentiment & beyond)
- 🧠 Neural language models — word2vec, ELMo, BERT
"You shall know a word by the company it keeps." — J.R. Firth, 1957

See you, space cowboy… 🤠📡
#NLP #MachineLearning #KnowledgeGraphs #AI #Word2Vec #BERT @fizise @fiz_karlsruhe @KIT_Karlsruhe

This week, session 08 of #KDAI2026 lecture 08: NLP 04 went live.
From words to vectors, from vectors to meaning:
- 🔤 TF-IDF & sparse document vectors
- 🎲 Naive Bayes classification (spam, sentiment & beyond)
- 🧠 Neural language models — word2vec, ELMo, BERT
"You shall know a word by the company it keeps." — J.R. Firth, 1957

See you, space cowboy… 🤠📡
#NLP #MachineLearning #KnowledgeGraphs #AI #Word2Vec #BERT @fizise @fiz_karlsruhe @KIT_Karlsruhe

This week, session 08 of #KDAI2026 lecture 08: NLP 04 went live.
From words to vectors, from vectors to meaning:
- 🔤 TF-IDF & sparse document vectors
- 🎲 Naive Bayes classification (spam, sentiment & beyond)
- 🧠 Neural language models — word2vec, ELMo, BERT
"You shall know a word by the company it keeps." — J.R. Firth, 1957

See you, space cowboy… 🤠📡
#NLP #MachineLearning #KnowledgeGraphs #AI #Word2Vec #BERT @fizise @fiz_karlsruhe @KIT_Karlsruhe

This week, session 08 of #KDAI2026 lecture 08: NLP 04 went live.
From words to vectors, from vectors to meaning:
- 🔤 TF-IDF & sparse document vectors
- 🎲 Naive Bayes classification (spam, sentiment & beyond)
- 🧠 Neural language models — word2vec, ELMo, BERT
"You shall know a word by the company it keeps." — J.R. Firth, 1957

See you, space cowboy… 🤠📡
#NLP #MachineLearning #KnowledgeGraphs #AI #Word2Vec #BERT @fizise @fiz_karlsruhe @KIT_Karlsruhe

This week, session 08 of #KDAI2026 lecture 08: NLP 04 went live.
From words to vectors, from vectors to meaning:
- 🔤 TF-IDF & sparse document vectors
- 🎲 Naive Bayes classification (spam, sentiment & beyond)
- 🧠 Neural language models — word2vec, ELMo, BERT
"You shall know a word by the company it keeps." — J.R. Firth, 1957

See you, space cowboy… 🤠📡
#NLP #MachineLearning #KnowledgeGraphs #AI #Word2Vec #BERT @fizise @fiz_karlsruhe @KIT_Karlsruhe

🎷 3… 2… 1… Let's jam! 🚀

In this week's #KDAI2026 lecture, we dived deeper into NLP:
🛰️ Text similarity & edit distance, Levenshtein, cosine & Jaccard
🛰️ Regular expressions from Kleene & Thompson to ELIZA
🛰️ Tokenisation & normalisation: BPE, stemming vs. lemmatisation

Get your tokeniser right, or your model flies blind. See you, space cowboy… 🪐

#NLP #MachineLearning #AI #DeepLearning #LLM #knowledgegraphs #TeachingAI @fizise @fiz_karlsruhe #cowboybebop

🎷 3… 2… 1… Let's jam! 🚀

In this week's #KDAI2026 lecture, we dived deeper into NLP:
🛰️ Text similarity & edit distance, Levenshtein, cosine & Jaccard
🛰️ Regular expressions from Kleene & Thompson to ELIZA
🛰️ Tokenisation & normalisation: BPE, stemming vs. lemmatisation

Get your tokeniser right, or your model flies blind. See you, space cowboy… 🪐

#NLP #MachineLearning #AI #DeepLearning #LLM #knowledgegraphs #TeachingAI @fizise @fiz_karlsruhe #cowboybebop

🎷 3… 2… 1… Let's jam! 🚀

In this week's #KDAI2026 lecture, we dived deeper into NLP:
🛰️ Text similarity & edit distance, Levenshtein, cosine & Jaccard
🛰️ Regular expressions from Kleene & Thompson to ELIZA
🛰️ Tokenisation & normalisation: BPE, stemming vs. lemmatisation

Get your tokeniser right, or your model flies blind. See you, space cowboy… 🪐

#NLP #MachineLearning #AI #DeepLearning #LLM #knowledgegraphs #TeachingAI @fizise @fiz_karlsruhe #cowboybebop

🎷 3… 2… 1… Let's jam! 🚀

In this week's #KDAI2026 lecture, we dived deeper into NLP:
🛰️ Text similarity & edit distance, Levenshtein, cosine & Jaccard
🛰️ Regular expressions from Kleene & Thompson to ELIZA
🛰️ Tokenisation & normalisation: BPE, stemming vs. lemmatisation

Get your tokeniser right, or your model flies blind. See you, space cowboy… 🪐

#NLP #MachineLearning #AI #DeepLearning #LLM #knowledgegraphs #TeachingAI @fizise @fiz_karlsruhe #cowboybebop

🎷 3… 2… 1… Let's jam! 🚀

In this week's #KDAI2026 lecture, we dived deeper into NLP:
🛰️ Text similarity & edit distance, Levenshtein, cosine & Jaccard
🛰️ Regular expressions from Kleene & Thompson to ELIZA
🛰️ Tokenisation & normalisation: BPE, stemming vs. lemmatisation

Get your tokeniser right, or your model flies blind. See you, space cowboy… 🪐

#NLP #MachineLearning #AI #DeepLearning #LLM #knowledgegraphs #TeachingAI @fizise @fiz_karlsruhe #cowboybebop

This week, we started the Natural Language Processing section of our #KDAI2026 lecture, diving back into NLP history, looking at basic linguistic concepts, discussing NLP applications and focusing on NLP Techniques and challenges.

#NLP #AI #NLP @fiz_karlsruhe @fizise @KIT_Karlsruhe

This week, we started the Natural Language Processing section of our #KDAI2026 lecture, diving back into NLP history, looking at basic linguistic concepts, discussing NLP applications and focusing on NLP Techniques and challenges.

#NLP #AI #NLP @fiz_karlsruhe @fizise @KIT_Karlsruhe

This week, we started the Natural Language Processing section of our #KDAI2026 lecture, diving back into NLP history, looking at basic linguistic concepts, discussing NLP applications and focusing on NLP Techniques and challenges.

#NLP #AI #NLP @fiz_karlsruhe @fizise @KIT_Karlsruhe

This week, we started the Natural Language Processing section of our #KDAI2026 lecture, diving back into NLP history, looking at basic linguistic concepts, discussing NLP applications and focusing on NLP Techniques and challenges.

#NLP #AI #NLP @fiz_karlsruhe @fizise @KIT_Karlsruhe

This week, we started the Natural Language Processing section of our #KDAI2026 lecture, diving back into NLP history, looking at basic linguistic concepts, discussing NLP applications and focusing on NLP Techniques and challenges.

#NLP #AI #NLP @fiz_karlsruhe @fizise @KIT_Karlsruhe

In our #KDAI2026 lecture this week we were taking a tour de force from perceptrons to transformers, 60 years of neural networks in a ninety minutes lecture.

#lecture @fiz_karlsruhe @fizise @KIT_Karlsruhe #AI #llms #perceptron #neuralnetwork #transformer #bert #gpt #lstm #rnn #transferlearning #machinelearning #cowboybebop

In our #KDAI2026 lecture this week we were taking a tour de force from perceptrons to transformers, 60 years of neural networks in a ninety minutes lecture.

#lecture @fiz_karlsruhe @fizise @KIT_Karlsruhe #AI #llms #perceptron #neuralnetwork #transformer #bert #gpt #lstm #rnn #transferlearning #machinelearning #cowboybebop

In our #KDAI2026 lecture this week we were taking a tour de force from perceptrons to transformers, 60 years of neural networks in a ninety minutes lecture.

#lecture @fiz_karlsruhe @fizise @KIT_Karlsruhe #AI #llms #perceptron #neuralnetwork #transformer #bert #gpt #lstm #rnn #transferlearning #machinelearning #cowboybebop

In our #KDAI2026 lecture this week we were taking a tour de force from perceptrons to transformers, 60 years of neural networks in a ninety minutes lecture.

#lecture @fiz_karlsruhe @fizise @KIT_Karlsruhe #AI #llms #perceptron #neuralnetwork #transformer #bert #gpt #lstm #rnn #transferlearning #machinelearning #cowboybebop

In our #KDAI2026 lecture this week we were taking a tour de force from perceptrons to transformers, 60 years of neural networks in a ninety minutes lecture.

#lecture @fiz_karlsruhe @fizise @KIT_Karlsruhe #AI #llms #perceptron #neuralnetwork #transformer #bert #gpt #lstm #rnn #transferlearning #machinelearning #cowboybebop

🎷 Session 03 of #KDAI2026 - Basic Machine Learning II went live today.
This week's transmission lands on 3 foundational algorithms:
🔭 k-Means Clustering — when there are no labels, let the data speak for itself
📉 Linear Regression — drawing the straightest line through the chaos
🌳 Decision Trees — asking the right questions in the right order.

Because every good lecture deserves a soundtrack. 🎶

#AI #MachineLearning #AIeducation #LehreKIT #CowboyBebop @fiz_karlsruhe @fizise

🎷 Session 03 of #KDAI2026 - Basic Machine Learning II went live today.
This week's transmission lands on 3 foundational algorithms:
🔭 k-Means Clustering — when there are no labels, let the data speak for itself
📉 Linear Regression — drawing the straightest line through the chaos
🌳 Decision Trees — asking the right questions in the right order.

Because every good lecture deserves a soundtrack. 🎶

#AI #MachineLearning #AIeducation #LehreKIT #CowboyBebop @fiz_karlsruhe @fizise

🎷 Session 03 of #KDAI2026 - Basic Machine Learning II went live today.
This week's transmission lands on 3 foundational algorithms:
🔭 k-Means Clustering — when there are no labels, let the data speak for itself
📉 Linear Regression — drawing the straightest line through the chaos
🌳 Decision Trees — asking the right questions in the right order.

Because every good lecture deserves a soundtrack. 🎶

#AI #MachineLearning #AIeducation #LehreKIT #CowboyBebop @fiz_karlsruhe @fizise

🎷 Session 03 of #KDAI2026 - Basic Machine Learning II went live today.
This week's transmission lands on 3 foundational algorithms:
🔭 k-Means Clustering — when there are no labels, let the data speak for itself
📉 Linear Regression — drawing the straightest line through the chaos
🌳 Decision Trees — asking the right questions in the right order.

Because every good lecture deserves a soundtrack. 🎶

#AI #MachineLearning #AIeducation #LehreKIT #CowboyBebop @fiz_karlsruhe @fizise

🎷 Session 03 of #KDAI2026 - Basic Machine Learning II went live today.
This week's transmission lands on 3 foundational algorithms:
🔭 k-Means Clustering — when there are no labels, let the data speak for itself
📉 Linear Regression — drawing the straightest line through the chaos
🌳 Decision Trees — asking the right questions in the right order.

Because every good lecture deserves a soundtrack. 🎶

#AI #MachineLearning #AIeducation #LehreKIT #CowboyBebop @fiz_karlsruhe @fizise

So, what did we learn in last week's lecture?
(1) The bounty log (history of AI)
(2) Symbolic vs subsymbolic (The two schools)
(3) The mechanics of the chase (ML types)
(4) The black box evaluation
Stay tuned for this weeks lecture on traditional ML technologies (k-Means, linear regression, decision trees)

#AI #machinelearning #cowboybebop #HistoryOfAI #modelEvaluation @fiz_karlsruhe @fizise #lecture #KDAI2026

So, what did we learn in last week's lecture?
(1) The bounty log (history of AI)
(2) Symbolic vs subsymbolic (The two schools)
(3) The mechanics of the chase (ML types)
(4) The black box evaluation
Stay tuned for this weeks lecture on traditional ML technologies (k-Means, linear regression, decision trees)

#AI #machinelearning #cowboybebop #HistoryOfAI #modelEvaluation @fiz_karlsruhe @fizise #lecture #KDAI2026

So, what did we learn in last week's lecture?
(1) The bounty log (history of AI)
(2) Symbolic vs subsymbolic (The two schools)
(3) The mechanics of the chase (ML types)
(4) The black box evaluation
Stay tuned for this weeks lecture on traditional ML technologies (k-Means, linear regression, decision trees)

#AI #machinelearning #cowboybebop #HistoryOfAI #modelEvaluation @fiz_karlsruhe @fizise #lecture #KDAI2026

So, what did we learn in last week's lecture?
(1) The bounty log (history of AI)
(2) Symbolic vs subsymbolic (The two schools)
(3) The mechanics of the chase (ML types)
(4) The black box evaluation
Stay tuned for this weeks lecture on traditional ML technologies (k-Means, linear regression, decision trees)

#AI #machinelearning #cowboybebop #HistoryOfAI #modelEvaluation @fiz_karlsruhe @fizise #lecture #KDAI2026

So, what did we learn in last week's lecture?
(1) The bounty log (history of AI)
(2) Symbolic vs subsymbolic (The two schools)
(3) The mechanics of the chase (ML types)
(4) The black box evaluation
Stay tuned for this weeks lecture on traditional ML technologies (k-Means, linear regression, decision trees)

#AI #machinelearning #cowboybebop #HistoryOfAI #modelEvaluation @fiz_karlsruhe @fizise #lecture #KDAI2026

Furthermore, we were discussing overfitting as another major problem with machine learning. SImply memorising the data doesn't help, when you have to make predictions over unknown data. When overfitting, the model looses the ability to generalise...

#AI #lecture #machine learning #KDAI2026 #overfitting #datascience #data @fiz_karlsruhe @fizise #knowledge

Furthermore, we were discussing overfitting as another major problem with machine learning. SImply memorising the data doesn't help, when you have to make predictions over unknown data. When overfitting, the model looses the ability to generalise...

#AI #lecture #machine learning #KDAI2026 #overfitting #datascience #data @fiz_karlsruhe @fizise #knowledge

Furthermore, we were discussing overfitting as another major problem with machine learning. SImply memorising the data doesn't help, when you have to make predictions over unknown data. When overfitting, the model looses the ability to generalise...

#AI #lecture #machine learning #KDAI2026 #overfitting #datascience #data @fiz_karlsruhe @fizise #knowledge

Furthermore, we were discussing overfitting as another major problem with machine learning. SImply memorising the data doesn't help, when you have to make predictions over unknown data. When overfitting, the model looses the ability to generalise...

#AI #lecture #machine learning #KDAI2026 #overfitting #datascience #data @fiz_karlsruhe @fizise #knowledge

Furthermore, we were discussing overfitting as another major problem with machine learning. SImply memorising the data doesn't help, when you have to make predictions over unknown data. When overfitting, the model looses the ability to generalise...

#AI #lecture #machine learning #KDAI2026 #overfitting #datascience #data @fiz_karlsruhe @fizise #knowledge

This week we were discussing the main challenges of Machine Learning in the #KDAI2026 lecture. It should be very obvious that "bad data quality leads to bad results" :)
However, we were also talking about insufficient number of data, non-representative data, irrelevant features, overfitting and various forms of bias.

@fiz_karlsruhe #AI #machinelearning #unicorn #dataquality #lecture #datascience

This week we were discussing the main challenges of Machine Learning in the #KDAI2026 lecture. It should be very obvious that "bad data quality leads to bad results" :)
However, we were also talking about insufficient number of data, non-representative data, irrelevant features, overfitting and various forms of bias.

@fiz_karlsruhe #AI #machinelearning #unicorn #dataquality #lecture #datascience

This week we were discussing the main challenges of Machine Learning in the #KDAI2026 lecture. It should be very obvious that "bad data quality leads to bad results" :)
However, we were also talking about insufficient number of data, non-representative data, irrelevant features, overfitting and various forms of bias.

@fiz_karlsruhe #AI #machinelearning #unicorn #dataquality #lecture #datascience

This week we were discussing the main challenges of Machine Learning in the #KDAI2026 lecture. It should be very obvious that "bad data quality leads to bad results" :)
However, we were also talking about insufficient number of data, non-representative data, irrelevant features, overfitting and various forms of bias.

@fiz_karlsruhe #AI #machinelearning #unicorn #dataquality #lecture #datascience

This week we were discussing the main challenges of Machine Learning in the #KDAI2026 lecture. It should be very obvious that "bad data quality leads to bad results" :)
However, we were also talking about insufficient number of data, non-representative data, irrelevant features, overfitting and various forms of bias.

@fiz_karlsruhe #AI #machinelearning #unicorn #dataquality #lecture #datascience