21 hours (usually 3 days including breaks)
- Understanding of statistics.
- Familiarity with multivariate calculus and basic linear algebra.
- Some experience with probabilities.
- Data analysts
- PhD students, researchers and practitioners
This instructor-led, live course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.
The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired.
Probability Theory, Model Selection, Decision and Information Theory
Linear Models for Regression and Classification
Sparse Kernel Machines
Mixture Models and EM
Continuous Latent Variables
Summary and Conclusion