Machine learning focuses on the development of algorithms and statistical models that can enable computers to learn from and make predictions or decisions without being explicitly programmed. Common sub-topics include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
Machine Learning usually requires linear algebra, calculus and proficiency in Computer Programming.
Stanford University
Spring 2023
This course focuses on data mining and machine learning algorithms for large scale data analysis. The emphasis is on parallel algorithms with tools like MapReduce and Spark. Topics include frequent itemsets, locality sensitive hashing, clustering, link analysis, and large-scale supervised machine learning. Familiarity with Java, Python, basic probability theory, linear algebra, and algorithmic analysis is required.
No concepts data
+ 17 more conceptsPrinceton University
Spring 2019
This introductory course focuses on machine learning, probabilistic reasoning, and decision-making in uncertain environments. A blend of theory and practice, the course aims to answer how systems can learn from experience and manage real-world uncertainties.
No concepts data
+ 21 more conceptsPrinceton University
Fall 2017
A thorough introduction to machine learning principles such as online learning, decision making, gradient-based learning, and empirical risk minimization. It also explores regression, classification, dimensionality reduction, ensemble methods, neural networks, and deep learning. The course material is self-contained and based on freely available resources.
No concepts data
+ 14 more conceptsStanford University
Winter 2023
This comprehensive course covers various machine learning principles from supervised, unsupervised to reinforcement learning. Topics also touch on neural networks, support vector machines, bias-variance tradeoffs, and many real-world applications. It requires a background in computer science, probability, multivariable calculus, and linear algebra.
No concepts data
+ 32 more conceptsStanford University
Winter 2023
An in-depth study of probabilistic graphical models, combining graph and probability theory. Equips students with the skills to design, implement, and apply these models to solve real-world problems. Discusses Bayesian networks, exact and approximate inference methods, etc.
No concepts data
+ 14 more concepts