In this era of big data, there is an increasing need to develop and deploy algorithms that can analyze and identify connections in that data. Using machine learning (a subset of artificial intelligence) it is now possible to create computer systems that automatically improve with experience. This technology has numerous real-world applications including robotic control, data mining, autonomous navigation, and bioinformatics.

This course features classroom lectures directly from the graduate course CS229, along with assignments adapted from the original course with additional support and guidance.

What you will learn:

• Supervised Learning (Linear and Logistic Regression, General Linearized Models (GLMs), Gaussian Discriminant Analysis (GDA), Generative/Discriminative Learning, Neural Networks, Support Vector Machines (SVM))
• Unsupervised Learning (Expectation-Maximization (K-Means, etc.), Principal Component Analysis (PCA), Dimensionality Reduction)
• Machine learning strategy (regularization, model selection and cross validation, empirical risk minimization, ML algorithm diagnostics, error analysis, ablative analysis)