Own implementation of machine learning algorithms:
| Algorithm | Code | Experiments | Report | Source |
|---|---|---|---|---|
| linear regression | reg.py | self-reg.ipynb | — | Andrew Ng’s old course, new one |
| binary logistic regression | logistic.py | self-log.ipynb | — | Andrew Ng’s old course, new one |
| one vs all logistic regression | classification.py | classification.ipynb | — | Andrew Ng’s old course, new one |
| k-means | clust.py | clust.ipynb | — | Alexander Dyakonov’s mini-course |
| knn | knn | experiments.ipynb | — | AIMasters ML course |
| SGD | sgd | experiments.ipynb | report.pdf | University assignment |
| decision tree | tree.ipynb | — | — | AIMasters ML course |
| random forest and gradient boosting regression | ensembles.py | experiments.ipynb | report.pdf | University assignment |
| GLAD (EM algo) | glad.py | derivation_experiments.ipynb | — | University assignment |
| word alignment (EM algo) | em-word-alignment | derivation_experiments.ipynb | — | University assignment |
| average precision subtleties | average-precision-comparison.ipynb | — | me | |
| PCA | pca | testbed.ipynb | pca | me |