An introduction to machine learning and signal processing for medical imaging and big data analytics. Overview of medical image reconstruction, registration, denoising, deblurring, and segmentation. Machine learning: supervised vs. unsupervised methods, training, testing, and cross-validation. Statistical estimation: least squares, maximum likelihood, and Bayesian methods. Regularization, overfitting and underfitting, and bias-variance trade-off. Numerical optimization: gradient descent, preconditioning, stochastic gradient descent. Clustering and classification. Deep learning: multilayer perceptrons, convolutional neural networks, recurrent nerural networks, autoencoders, and reinforcement learning. Deep learning software suites. Application of data science tools to medical datasets.