Knowledge-based Systems - AI for EPI

Artificial Intelligence will change all processes of life substantially. Understanding the potentials and pitfalls is essential for applying or rejecting AI-based solutions in every situation of your future career. Introductions to AI are usually targeted at Statisticians or Computer Scientist. In this course I would like to lay to teach the conceptual foundations without the low level engineering or math problems associated, while still staying as actionable as possible. Wish us luck :-)

This course is target at Healthcare Professionals with basic skills in an interpreted programming language (R, Python).

Course Goals: - Understand and apply the basics of Knowledge representation - Enable Specification of Software and AI Needs, Basic Implementation Skills - Understand opportunities and limitations of ML and AI in Public Health

Objectives

A. Knowledge Representation

• Terminologies, Vocabularies and Taxonomies
• Ontologies and Knowledge Graphs
• Linked Data and Knowledge Representation Languages
• Building Knowledge-based Systems

B. Logic, Inference, and Statistical Learning

• Overview on supervised and unsupervised ML methods
• Liner Regression
• Classification
• GAN, Deep Learning
• Generative AI

Syllabus

A. Logic and Knowledge Representation

1. Introduction

Lecture:

• From Data to Knowledge with Cognitive Science
• Syntax, Semantics, Pragmatics

Hands-on Activation: Visual Introduction to Machine Learning

• http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
• http://www.r2d3.us/visual-intro-to-machine-learning-part-2/

2. History of AI

Lecture:

• Introduction and History of AI: Chapter 1

3. Intelligent Agents

Lecture:

• Intelligent Agents: Chapter 2
• Rationality and Environments
• Agents: Simple, Model-based, Goal-based, Utility-based, Learning

Exercise:

• Sudoku (1/2): https://medium.com/@co.2020.prkude/formulation-of-csp-problem-sudoku-puzzle-7d5e1d547382, https://github.com/norvig/pytudes/blob/main/ipynb/Sudoku.ipynb

4. Problem-solving

Lecture Chapter 3-6:

• Search (8-Block)
• Complex Search (8-Queens)
• Constraint Satisfaction (Sudoku)
• Adversarial Search and Games (Advanced Chess, Backgammon as Stochastic Game)

Exercise:

• Sudoku (2/2) as a Constraint Satisfaction Problem, Backtracking Algorithm in Depth

5. Propositional Logic: Knowledge Representation

Lecture Chapter 7a:

• Propositional Logic
• Semantics
• A simple Knowledge Base

Exercise:

• Wumpus World

6. Propositional Logic: Inference

Lecture Chapter 7b:

• Inference
• Forward and Backward Chaining: https://builtin.com/artificial-intelligence/forward-chaining-vs-backward-chaining

Exercise:

• Wumpus World

7. First-Order Logic: Ontologies and Knowledge Representation

Lecture Chapter 10:

• Knowledge Representation
• History and Theory of Taxonomies, Ontologies and Semantic Networks
• Linked Data and Languages for Knowledge Representations (OWL, RDF)

Exercise:

• Building an Ontology

8. Ontologies, Information Retrieval and Inference

Recap and Interactive:

• Building an Ontology
• Ideating Projects

Lecture:

• Inference
• SPARQL Queries

Exercise:

• Querying DBPedia

9. Statistical Learning

• Statistical Learning (Overview and Basic understanding)

  • Supervised vs. Unsupervised Learning
  • Interpretability vs. Flexibility
  • Common Errors (Overfitting,...)

• Linear Regression

• Classification

Reading:

• https://cran.r-project.org/web/packages/rdflib/vignettes/rdf_intro.html

10. Building Ontologies with Statistical Learning

• Mining Ontologies from Text
• Text-Mining
• Applications in Life-Sciences

11. Ontologies and Deep Learning

• Applications in Life Sciences
• Regulatory Restrictions on Deep Learning in Healthcare

12. Ontologies and Generative AI

• Applications in Life Sciences
• Regulatory Restrictions
• Discussion: Personalized Medicine and Generative AI

Literature

• A | Norvig, Russel (2021). Artificial Intelligence - A Modern Approach: https://ebookcentral.proquest.com/lib/th-deggendorf/reader.action?docID=6563527&ppg=227
• B | Hastie et al. (2021). An Introduction to Statistical Learning: https://www.statlearning.com/