This course provides an opportunity to graduate students with both the breadth and depth in the area of machine learning and deep learning applied to biomedical applications. The course firstly introduces basic machine learning algorithms, including supervised learning (e.g., logistic regression, naïve bayes, decision trees, etc.) and unsupervised learning (k-means, hierarchical clustering). It will be followed by describing other fundamental concepts, such as classifier evaluation and statistical testing to compare classifiers. The next part is the study of different deep learning models (in seminar style) for various biomedical applications that deals with multiple types of data, including and not limited to biosignals, physiological data, environmental data, speech, text, images, and videos. Different types of supervised and unsupervised frameworks for sequential and non-sequential data will be discussed, including Feed-forward neural network, Convolution neural networks, Autoencoders, Long Short-Term Memory, Temporal Convolution Network. In the last part, advanced deep learning architectures applied to biomedical application will be discussed, including Generative Adversarial Network and Contrastive Learning. The course will comprise of programming assignment (2), paper critiques, and a group project (team of 1 to 3 persons).