Are you interested in the exciting topic of pattern recognition an deep learning? With the certificate course pattern recognition an deep learning, you will learn the basics of statistical and neural pattern recognition, linear and nonlinear classifiers, kernel methods, feature extraction and applications and system performance evaluation. With the certificate course pattern recognition and deep learning you will add value to your portfolio.
Pattern Recognition and Deep Learning
- Knowledge transfer at a high academic level
- Flexible blended learning format
- Acquire knowledge while working
Dates / Location / Cost
| Date | Location | Costs |
|---|---|---|
| 01.10.2024 - 31.03.2025 Course ID: UUL-SST-ZERT-PRDL-202410 | Ulm & online | 1.170,00 € |
Institution and Location
Institution
School of Advanced Professional StudiesUniversität Ulm
Location
Oberberghof 7, 89081 Ulm / OnlineTarget Audience and Prerequisites
Target Audience
Graduates with a Bachelor’s degree with basic knowledge in programming and basic concepts of analysis, linear algebra, and probability.Prerequisites
Requirement is a first academic degreeContent and Learning Objectives
Content
In this course the basic topics on statistical pattern recognition and deep neural networks are introduced:- Introduction to statistical and neural pattern recognition
- Linear and nonlinear classifiers
- Kernel methods and learning deep neural network
- Feature extraction, selection and reduction
- Applications and system performance evaluation
Programme Details
Self-study using learning materials provided on a learning platform (books, instructional videos, exercises), online consultation hours, topical exchange in online forums, final examLearning Objectives
Students acquire knowledge about different methods and algorithms of pattern recognition and deep artificial neural networks. In exercises, students are able to implement the basic algorithms, will apply pattern recognition principles to technical applications, and learn how to evaluate the performance of classifiers.Course Format, Certification, Quality Assurance
Course Format
Blended-Learning
Workload
A total workload of approx. 180 hours for self-study, supervision, participation in the attendance day, exam preparation and exam is to be expected.Creditpoints (ECTS)
6
Language
English
Academic Recognition
After enrollment a recognition of this course is possible.Lecturers
Prof. Dr. Friedhelm Schwenker
Universität Ulm
Universität Ulm
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