Undergraduate courses

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)