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https://github.com/saikat-roy/uni-bonn-pattern-recognition
Code from the Pattern Recognition (1) at University of Bonn (SS 19): Normal Distribution fitting, MLE, Fractal Dimensions, Bayesian Regression NN, kDTrees, k-mean & spectral clustering, PCA, LDA
https://github.com/saikat-roy/uni-bonn-pattern-recognition
bonn lecture machine-learning pattern-recognition python3 university visualization
Last synced: 10 days ago
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Code from the Pattern Recognition (1) at University of Bonn (SS 19): Normal Distribution fitting, MLE, Fractal Dimensions, Bayesian Regression NN, kDTrees, k-mean & spectral clustering, PCA, LDA
- Host: GitHub
- URL: https://github.com/saikat-roy/uni-bonn-pattern-recognition
- Owner: saikat-roy
- License: apache-2.0
- Created: 2019-04-12T00:13:09.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-11-16T08:21:51.000Z (about 5 years ago)
- Last Synced: 2024-11-05T21:45:32.606Z (about 2 months ago)
- Topics: bonn, lecture, machine-learning, pattern-recognition, python3, university, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 6.91 MB
- Stars: 0
- Watchers: 7
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# University of Bonn: Pattern Recognition (1) SS 2019
Code from the Pattern Recognition (1) lecture at University of Bonn (SS19). A lot of algorithms were implemented in Python during the lecture without the usage of libraries like scikit-learn (atleast too often). The focus during coding was vectorized implementations instead of the heavy use of iterations.
The practical part of the lecture consisted of 3 projects which involved the implementation of a breadth of machine learning and pattern recognition algorithms.
## Project 1
1. [Matplotlib visualization of 1D data](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project1/task1_1)
2. [Fitting 1D Gaussian distribution to data](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project1/task1_2)
3. [Maximum likelihood estimate of Weibull Distribution](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project1/task1_3)
4. [Drawing unit circles with different distance metrics](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project1/task1_4)
5. [Estimating dimensions of Fractal objects in an image](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project1/task1_5)
## Project 2
1. [Least Squares Regression for missing value prediction](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project2/task2_1)
2. [Conditional Expectation for missing value prediction](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project2/task2_2)
3. [Bayesian regression for missing value prediction](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project2/task2_3)
4. [Boolean functions and boolean Fourier transform](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project2/task2_4_1)
5. [Nearest neighbour classifier](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project2/task2_4_2)
6. [Nearest neighbour search using KD-Trees](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project2/task2_5)
## Project 3
1. [k-means clustering](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project2/Task3_1)
2. [Spectral clustering](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project2/Task3_2)
3. [Dimensionality reduction using PCA and multiclass-LDA](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project2/Task3_3)
4. [Non-monotonous activation units for XOR problem using a single neuron](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project2/Task3_4)
5. [Exploring numerical instabilities with polynomial regression and normalization techniques](https://github.com/saikat-roy/Uni-Bonn-Pattern-Recognition/tree/master/Project3/Task3_5_saikatroy)