Lecture · Informatics / Mathematics & Natural Sciences · WS 2022/23

Introduction to Machine Learning

Machine Learning has gained a lot of momentum within development organizations that are actively looking for innovative solutions to leverage their data to identify new levels of understanding their operations and processes. Machine learning is a subfield of Artificial Intelligence where the machine learns from data rather than from explicit programming.

About this course

The course description, objectives, audience, prerequisites, materials and evaluation are the same every semester and live on the course page.

AboutLearning objectivesPrerequisitesMaterialsEvaluation

Schedule

DateTopicMaterialSelf-reading
29.09.2022Introduction (Ch1)Lecture01
06.10.2022Foundations: Probability (Ch2)Lecture02Statistics (Ch4) + Decicion Theory (Ch5)
13.10.2022Foundations: Probability (Ch3)Assignment01Information Theory (Ch6) + Linear Algebra (Ch7) + Optimization (Ch8)
20.10.2022Linear Models: Logistic Regression (Ch10)Lecture03
27.10.2022Linear Models: Logistic Regression (Ch10) – Cont.Assignment02Linear Discriminative Analysis (Ch9)
03.11.2022Linear Models: Linear Regression (Ch11)Lecture04Generalized Linear Models (Ch12)
10.11.2022Linear Models: Linear Regression (Ch11) – Cont.Lab01Generalized Linear Models (Ch12)
17.11.2022Mid-term Evaluation
01.12.2022Neural Networks: NNs for Tabular Data (Ch13)Lecture05
08.12.2022Neural Networks: NNs for Tabular Data (Ch13) – Cont. + LabLab02
15.12.2022Neural Networks: NNs for Imaging Data (Ch14)Lecture06
22.12.2022Beyond Supervised Learning: Dimensionality Reduction (Ch20) + Clustering (Ch21)Lecture07Recommender Systems (Ch22)
29.12.2022Hands-on Experience on the projects
05.01.2023Course Recap.Lecture08
19.01.2023Final Project Evaluation

References

Projects

Interested in the Medical Imaging community!