Machine Learning Review
Coursework
Great Notes on most of these topics from Penn CIS 520 2016 class.
- Probability
- Nearest Neighbour
- Decision Trees
- Random Forests
- Naive Bayes
- Logistic Regression
- Kernels
- SVMs
- PCA, Eigen decomposition, SVD
- t-SNE
- Clustering: K-Means | EM | K-medoids | Hierarchical | Spectral - paper on normalized cuts
- Relationship between SVD and $\min_{\mathbf{x}} A\mathbf{x}~s.t.~\mathbf{x}^T\mathbf{x} = 1$ - Appendix A of Hartley-Zisserman
- Softmax, cross entropy loss, and derivatives
- Bidirectional RNNs, and LSTMs.
- Generative models like VAE, GAN, diffusion probabilistic models.