ML handwritten notes

Unleash Your Inner Genius: Master ML with Highly Curated Notes

I have carefully chosen and thoughtfully organised ML concepts from various online resources, books and a bunch of my Post-Grad courses at IISc Bangalore. Generally speaking, below are the concepts covered:

Classification foundations

• Perceptron learning algorithm (with convergence proof)
• Nearest-neighbour classifier
• Bayes classifier / Bayesian decision theory & risk minimization
• Neyman–Pearson classifier
• ROC curves

Probabilistic modeling & estimation

• Maximum likelihood estimation (MLE)
• Maximum a posteriori (MAP) estimation — priors, posteriors, likelihood
• Gaussian / multivariate Gaussian distributions
• Expectation–Maximization (EM) algorithm
• Gaussian mixture models

Regression & linear models

• Linear regression / least squares (normal equations)
• Invertibility of AᵀA and the pseudo-inverse
• Logistic regression
• L2 regularization
• Bias–variance tradeoff

Neural networks & deep learning

• Backpropagation (forward & backward pass)
• Dropout
• Batch normalization (internal covariate shift)
• ResNets / residual connections (vanishing gradients)
• Convolution
• Adversarial examples

Kernel methods & SVMs

• Support vector machines (margins, support vectors)
• Lagrangian duality / dual formulation
• Kernel functions & positive-definite (Mercer) kernels
• Kernel PCA

Dimensionality reduction & feature selection

• Principal component analysis (PCA)
• Kernel PCA
• Filter-based feature selection

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