On this page you will find undergraduate course in AI given at Lund University. More information about the courses and whether they are open for registration can be found at: Kurser och program | Lunds universitet

AI in Society (SIMS40) Autumn – 15 credits. Course description (pdf)

Applied Machine Learning (EDAN95) Autumn – 7,5 credits. Course description (pdf)

Applied Robotics (FRTF20) Autumn – 7,5 credits. Course description (pdf)

Artificial Intelligence: An Intersectional Perspective (RÄSN12) Autumn – 7.5 credits. Course description (pdf)

Artificial Intelligence (EDAP01) Spring – 7,5 credits. Course description (pdf)

Bayesian Methods (STAE02) Autumn – 7,5 credits. Course description (pdf)

Bioinformatics: Bioinformatics and Sequence Analysis (BINP11) Autumn – 7,5 credits. Course description (pdf)

Bioinformatics: Programming in Python (BINP16) Autumn – 7,5 credits. Course description (pdf)

Biology: Modelling Biological Systems (BIOS13) Autumn – 7,5 credits. Course description (pdf)

Business Analytics (STAE03) Autumn – 7,5 credits. Course description (pdf)

Business Processes and Artificial Intelligence (INFN65) Autumn – 7,5 credits. Course description (pdf)

Cognitive Science: Neuro Modelling, Cognitive Robotics and Agents (KOGP05) Autumn – 7,5 credits. Course description (pdf)

Cognitive Science: Theories and Models in Cognitive science (KOGP09) Autumn – 7,5 credits. Course description (pdf)

Computational Approaches in the Social Sciences (SIMM71) PERIOD, YEAR – 7.5 credits. Course description (pdf)

Computer Vision (FMAN95) Spring – 7,5 credits. Course description (pdf)

Digital Cultures: Theories - Introduction (DIKA11) Spring – 7,5 credits. Course description (pdf)

Digitalisation and AI from an Organisational and Societal Perspective (INFA40) Autumn – 7,5 credits. Course description (pdf)

European Data Protection Law ( HARG25) Autumn – 15 credits. Course description (pdf)

European Patent Law ( JAEN61) Autumn – 15 credits. Course description (pdf)

Finance: Financial Econometrics and Machine Learning (NEKN96) PERIOD, YEAR – 7.5 credits. Course description (pdf)

Health Law (JUCN32) Autumn – 15 credits. Course description (pdf)

Intellectual Property, Digitalisation and Artificial Intelligence (HARN52) Autumn – 7,5 credits. Course description (pdf)

Intelligent Autonomous Systems (EDAP20) Autumn – 7,5 credits. Course description (pdf)

Introduction to Machine Learning, Systems and Control (FRTF25) Autumn – 7,5 credits. Course description (pdf)

Language Technology (EDAN20) Autumn – 7,5 credits. Course description (pdf)

Law and Artificial Intelligence (AI) (HARA35) Autumn – 7,5 credits. Course description (pdf)

Learning-Based Control (FRTN75) Autumn – 7,5 credits. Course description (pdf)

Linear and Combinatorial Optimization (FMAF35) Spring – 6 credits. Course description (pdf)

Machine Learning (FMAN45) Spring – 7,5 credits. Course description (pdf)

Mathematical Statistics: Linear and Logistic Regression (MASM22) Spring – 7,5 credits. Course description (pdf)

Mathematical Statistics: Nonparametric Inference (MASM27) Spring – 7,5 credits. Course description (pdf)

Mathematical Statistics: Spatial Statistics with Image Analysis (MASM25) Autumn – 7,5 credits. Course description (pdf)

Mathematical Statistics: Time Series Analysis (MASM17) Autumn – 7,5 credits. Course description (pdf)

Mathematics: Image Analysis (MATC20) Autumn – 7,5 credits. Course description (pdf)

Medical Image Analysis (FMAN30) Autumn – 7,5 credits. Course description (pdf)

Memory Technology for Machine Learning (EITP25) Spring – 7,5 credits. Course description (pdf)

Modelling and Learning from Data (FRTN65) Autumn – 7,5 credits. Course description (pdf)

Monte Carlo and Empirical Methods for Stochastic Inference (MASM11) Spring – 7,5 credits. Course description (pdf)

Neuroengineering (BMEF20) , – 7,5 credits. Course description (pdf)

Numerical Analysis: Numerical Linear Algebra (NUMB11) Spring – 7,5 credits. Course description (pdf)

Optimization for Learning (FRTN50) Autumn – 7,5 credits. Course description (pdf)

Project in Systems, Control and Learning (FRTN70) , – 7,5 credits. Course description (pdf)

Public Relations (SKOP21) Autumn – 7,5 credits. Course description (pdf)

Radar and Remote Sensing (EITN90) Spring – 7,5 credits. Course description (pdf)

Satellite Remote Sensing (NGEN08) Spring – 15 credits. Course description (pdf)

Service Robotics (TNSN01) Autumn – 7,5 credits. Course description (pdf)

Smart City Governance: AI Ethics in a Spatial Context (VFTN75) PERIOD, YEAR – 7,5 credits. Course description (pdf)

Statistics: Advanced Machine Learning (STAN52) Autumn – 7.5 credits. Course description (pdf)

Statistics: Deep Learning and Artificial Intelligence Methods (STAN47) Autumn – 7,5 credits. Course description (pdf)

Statistics: Machine Learning from a Regression Perspective (STAN51) Autumn – 7.5 credits. Course description (pdf)

Strategic Communication and digital media - Culture and Society (SKOB31) Autumn – 7,5 credits. Course description (pdf)

Strategic Communication: AI, Cognition and Culture (KOMC30) Autumn – 15 credits. Course description (pdf)

Taxation in the Digital Era (JUDN23) Autumn – 15 credits. Course description (pdf)

Theoretical Physics: Introduction to Artificial Neural Networks and Deep Learning (FYTN14) Autumn – 7,5 credits. Course description (pdf)