Introduction to Pattern Recognition
01.10.2018
This course consisted of an exercise and a lecture part.
The lecture covered the following topics:
- Statistical Basics
- Bayes Theorem
- Features
- PCA Perceptron
- Neural Networks
- Decision Trees
- Markov Models
- Evaluation
- Clustering
The accompanying exercise taught the implementation of the topics dealt with in the lecture with four assignments. They were implemented using Jupyter Notebook and Python.
- Vectors, Gaussians, Plotting, Matrices, Images, Feature Extraction
- kNN & Bayes
- Discriminant functions, PCA, Perceptron
- Clustering, Mahalanobis distance, Evaluation