Module MA4320-KP05
Optimisation methods for machine learning (OptvML)
Duration
1 Semester
Turnus of offer
irregularly
Credit points
5
Course of studies, specific fields and terms:
- Bachelor CLS 2016, optional subject, mathematics
- Master CLS 2016, optional subject, mathematics
- Bachelor CLS 2023, optional subject, mathematics
- Master CLS 2023, optional subject, mathematics
Classes and lectures:
- MA4320-Ü: Optimisation methods for machine learning (exercise, 1 SWS)
- MA4320-V: Optimisation methods for machine learning (lecture, 2 SWS)
Workload:
- 45 hours in-classroom work
- 85 hours private studies and exercises
- 20 hours exam preparation
Contents of teaching:
- Fundamentals of current machine learning methods and their applications in Artificial Intelligence
- First-order stochastic optimization methods, moment-based optimization methods
- Continuous-time modeling, Langevin dynamics
- Adaptive and moment-based optimization methods
- Higher-order optimization methods
- Deep learning from the perspective of optimal control
- Optimization on manifolds
- Information-geometric approaches
Qualification-goals/Competencies:
- Students understand the fundamentals of current machine learning methods
- Students have an overview of optimization methods for high-dimensional problems and know the mathematical tools for their analysis
- Students can view optimization methods from different mathematical perspectives
- Students have a basic understanding of differential and information geometry and their use in numerical optimization
- Interdisciplinary aspects:
- They can translate theoretical concepts into practical solutions.
- They have implementation experience.
- They are able to abstract practical problems.
Grading through:
- Written or oral exam as announced by the examiner
Responsible for this module:
Literature:
- Bottou, Curtis, Nocedal : Optimization Methods for Large-Scale Machine Learning SIAM
- Absil, Mahony, Sepulchre : Optimization Algorithms on Matrix Manifolds
- Amari : Information Geometry and Its Applications
Language:
- German and English skills required
Notes:
Admission requirements for taking the module:- None (the competences of the modules mentioned under Requires are needed for this module, but are not a formal prerequisite)
Admission requirements for participation in module examination(s):
- Successful completion of exercises and their presentation in accordance with the specifications at the beginning of the semester
Module Exam(s):
- MA4320-L1: Optimisation methods for machine learning, written exam, 90min, or oral exam, 30min, according to the lecturer, 100% of the module grade
Last Updated:
09.02.2026