About the course
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.
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)
- College Calculus, Linear Algebra: You should be comfortable taking (multivariable) derivatives and understanding matrix/vector notation and operations. We strongly recommend you review this baseline problem set from the Fall 2018 graduate course upon which much of this course is based. You should be familiar with the topics covered before enrolling in XCS229i.
- Basic Probability and Statistics: You should know the basics of probabilities, gaussian distributions, mean, and standard deviation.
Andrew Ng is Co-founder of Coursera, and an Adjunct Professor of Computer Science at Stanford University. His machine learning course is the MOOC that had led to the founding of Coursera! In 2011, he led the development of Stanford University’s main MOOC (Massive Open Online Courses) platform an...
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Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.