**AdaBoost**: AdaBoost: Fits a sequence of weak learners on repeatadly modified data. The modifications are based on errors made by previous learners scikit**Classification:**scikit**Distance metrics:**Euclidean, Cosine, Hamming, Manhattan tutorial**Expectation-maximization (EM)**: Algo assumes random components and computes for each point a probability of being generated by each component of the model. Then iteratively tweaks the parameters to maximize the likelihood of the data given those assignments. Example: Gaussian Mixture**Gaussian Mixtures:**anomaly detection example: future examples may look nothing like the past. This is where supervised learning differs because it assumes that future examples fall within the range of the training data**Gradient Boosting:**optimization of arbitrary differentiable loss functions.

converting notebook to html using

`jupyter nbconvert --to html --no-input --no-prompt '/content/drive/My Drive/Colab Notebooks/ml_notes.ipynb' --output ml_notes.html`