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
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
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/
Lecture:
- Introduction and History of AI: Chapter 1
Lecture:
- Intelligent Agents and Problem Solving: Chapter 2-6 a. Rationality and Environments c. Agents: Simple, Model-based, Goal-based, Utility-based, Learning b. Search (8-Block), Complex Search (8-Queens), Constraint Satisfaction (Sudoku), Adversarial Search and Games (Advanced Chess, Backgammon as Stochastic Game)
Exercise:
- Sudoku as a Constraint Satisfaction Problem: https://medium.com/@co.2020.prkude/formulation-of-csp-problem-sudoku-puzzle-7d5e1d547382, https://github.com/norvig/pytudes/blob/main/ipynb/Sudoku.ipynb
- History and Theory of Taxonomies, Ontologies and Semantic Networks
- Linked Data and Languages for Knowledge Representations (OWL, RDF)
- Exercise:
- Querying DBPedia
- Building an Ontology
-
Statistical Learning (Overview and Basic understanding)
- Supervised vs. Unsupervised Learning
- Interpretability vs. Flexibility
- Common Errors (Overfitting,...)
-
Linear Regression
-
Classification
-
Linear Models
- Resampling Methods
- Linear Model Selection
- Non-Linear Models
- Tree-based Methods
- Support Vector Machines
- Deep Learning
- Survival Analysis and Censored Data
- Unsupervised Learning
- Multiple Testing
A+B | 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/