Artificial Intelligence (AI) refers to systems that display intelligent behaviour by analysing their environment and taking actions to achieve specific goals. A recent success story within AI comes from statistical learning where data is automatically processed to learn key relationships.
The subject consists in a creative combination of mathematics and programming, where the use of data is always at the centre. Generic computer programs are used, which are adapted to application specific circumstances by automatically adjusting parameters of the program based on observed so-called training data.
- Approximate inference (ApI): many learning problems involve intractable integrals, requiring good approximation methods; includes Monte Carlo and variational inference.
- Causal learning (CL): causality is a fundamental notion in science and engineering; causal learning establishes cause-effect relationships from observations that can be empirically tested for their accuracy.
- Constraint programming (CP) is an AI approach to optimisation: modelling languages, high-level constraints, high-level types for decision variables, symmetry breaking.
- Explainable Artificial Intelligence (XAI): artificial intelligence models that follow the principles of transparency, interpretability, and explainability, and provide solutions that can be understood by humans.
- Knowledge representation & reasoning (KR): automated reasoning and theorem proving (ATP), knowledge compilation (KC), logic programming (LP).
- Large-scale optimisation in learning (LO): optimisation problems with a huge number of unknowns, common in e.g. deep learning.
- Local search (LS): meta-heuristics, modelling languages, search languages, solver design, autonomous search.
- Machine learning (ML) is about learning, reasoning, and acting based on data; in this way, machine learning is about programming by examples; important topics include clustering, deep learning, and inductive logic programming (ILP) for relational learning.
- Pattern recognition (PR) is the automated recognition of patterns and regularities in data, typically by the use of ML.
- Propositional satisfiability (SAT) and SAT modulo theories (SMT): trustworthy and verified solvers, proofs and certificates, competitions and evaluations.
- Probabilistic modelling (PM): mathematical models capable of representing uncertainties.
- Reinforcement learning (RL) studies algorithms capable of perceiving their environment, interpret it, take actions, and learn through trial and error.
- Social robotics (SR): human-robot interaction, socially intelligent robots, social artificial intelligence, multimodal interaction.
- Arena on Machine Learning (a department-wide umbrella): ML
- CAiM - Computer-assisted Applications in Medicine: ML, PR, XAI, RL
- MIDA – research group on Methods for Image Data Analysis: ML, PR, XAI
- Optimisation research group: CP, LS (= AI approaches to optimisation)
- Uppsala University Information Laboratory (InfoLab): ML
- Uppsala Social Robotics Lab: SR, XAI
- Johannes Borgström: PM, ApI
- Ginevra Castellano (also see her homepage): SR, XAI, ML
- Pierre Flener (also see his homepage): CP, LS, KC, ILP, LP
- Maria Andreina Francisco Rodriguez (also see her homepage): CP, KC
- Didem Gurdur Broo (also see her homepage): SR, XAI, KR
- Orcun Göksel: ML, PR, XAI, RL
- Joakim Lindblad (also see his homepage): ML, PR, XAI
- Matteo Magnani: ML
- Per Mattsson (also see his ): RL
- Justin Pearson (also see his homepage): CP, LS, ATP
- Thomas Schön (also see his homepage): ML, ApI, DL, RL, PM
- Ida-Maria Sintorn: ML, PR, XAI
- Jens Sjölund (also see his homepage): ML, PM, LO
- Nataša Sladoje (also see her homepage): ML, PR, XAI
- David Sumpter: ML
- Niklas Wahlström: ML, PM
- Tjark Weber (also see his homepage): ATP, SAT, SMT
- Katie Winkle (also see her homepage): SR
- Dave Zachariah: ML, PM, CL
- Prashant Singh (also see his homepage): ML, ApI, LS
- Carolina Wählby (also see her homepage): ML, PR
- Ginevra Castellano: Outstanding Associate Editor Award by Frontiers Robotics and AI), 2021
- Ginevra Castellano: 10-Year Technical Impact Award in 2019 at the ACM International Conference on Multimodal Interaction (ICMI) 2019, 2019
- Thomas Schön: Tage Erlanders prize for natural sciences and technology by The Royal Swedish Academy of Sciences (KVA), 2017
- Thomas Schön: Arnberg prize (Arnbergska priset) by The Royal Swedish Academy of Sciences (KVA), 2016.
- Thomas Schön is an elected member of The Royal Swedish Academy of Engineering Sciences (IVA), since 2018, and The Royal Society of Sciences at Uppsala, since 2018.
- Carolina Wählby is an elected member of The Royal Swedish Academy of Engineering Sciences (IVA), since 2017, and The Royal Society of Sciences at Uppsala, since 2017.
- 1DL010: Artificial Intelligence (7.5 credits): KR, ML, search
- 1DL340: Artificial Intelligence (5 credits): KR, ML, search
- 1DL360: Data Mining I (5 credits): ML, search
- 1DL370: Data Mining (7.5 credits): ML, search
- 1DL442: Combinatorial Optimisation and Constraint Programming: CP, LS
- 1DL451: Modelling for Combinatorial Optimisation: CP, LS, SAT, SMT
- 1DL481: Algorithms and Data Structures 3: LS, SAT, SMT
- 1MD032 / 1MD039: Intelligent Interactive Systems: SR
- 1MD120: Deep Learning for Image Analysis
- 1MD300: Social robotics and human-robot interaction: SR
- 1RT003: Advanced Probabilistic Machine Learning
- 1RT700: Statistical Machine Learning