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Courses
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G22.2565-001: Machine Learning and Pattern Recognition. Graduate course.
V22.0480-001: Introduction to Robotics. A hands-on undergraduate course on robotics and embedded systems.
Machine Learning and Pattern Recognition. Graduate course
V22.0480-001: Introduction to Robotics. A hands-on undergraduate course on robotics and embedded systems. Topics include sensors and actuators, microcontroler programming, basic introduction to control, forward and inverse kinematics, vision and image processing, and pattern recognition.
G22.2565-001: Machine Learning and Pattern Recognition. A Graduate course that introduces most major machine learning and pattern recognition methods: from Linear Classifiers to neural nets. From Boosting to Support Vector Machines. From Hidden Markov Models to graph transformer networks to Graphical Models. From Vector Quantization to Unsupervised Learning. Model selection and ensemble methods methods from Bayesian regularization to bagging to pruning to structural risk minimization.
G22.2565-001: Machine Learning and Pattern Recognition. (updated version of the Fall 2006 course). A Graduate course that introduces most major machine learning and pattern recognition methods: from Linear Classifiers to neural nets. From Boosting to Support Vector Machines. From Hidden Markov Models to graph transformer networks to Graphical Models. From Vector Quantization to Unsupervised Learning. Model selection and ensemble methods methods from Bayesian regularization to bagging to pruning to structural risk minimization.
G22.3033-003: Mobile Robotics. A seminar/workshop course on mobile robotics.
V22.0480-002: Introduction to Machine Learning and Pattern Recognition. An undergraduate Senior/Junior-level introduction to Machine Learning and Pattern Recognition. The course introduces most major machine learning and pattern recognition methods: from Linear Classifiers to neural nets. From Boosting to Support Vector Machines. From Hidden Markov Models to graph transformer networks to Graphical Models. From Vector Quantization to Unsupervised Learning. Model selection and ensemble methods methods from Bayesian regularization to bagging to pruning to structural risk minimization.
G22.2565-001: Machine Learning and Pattern Recognition. (updated version of the Fall 2005 course). A Graduate course that introduces most major machine learning and pattern recognition methods: from Linear Classifiers to neural nets. From Boosting to Support Vector Machines. From Hidden Markov Models to graph transformer networks to Graphical Models. From Vector Quantization to Unsupervised Learning. Model selection and ensemble methods methods from Bayesian regularization to bagging to pruning to structural risk minimization.
G22.3033-007: Mobile Robotics. A PhD-level seminar/workshop course on mobile robotics.
G22.3033-013: Advanced Machine Learning Seminar. A seminar-style course on advanced topics in Machine Learning.
G22.2565-001: Machine Learning and Pattern Recognition. (updated version of the Fall 2004 course). A Graduate course that introduces most major machine learning and pattern recognition methods: from Linear Classifiers to neural nets. From Boosting to Support Vector Machines. From Hidden Markov Models to graph transformer networks to Graphical Models. From Vector Quantization to Unsupervised Learning. Model selection and ensemble methods methods from Bayesian regularization to bagging to pruning to structural risk minimization.
V22.0480-006: Introduction to Machine Learning and Pattern Recognition. An undergraduate Senior/Junior-level introduction to Machine Learning and Pattern Recognition. The course introduces most major machine learning and pattern recognition methods: from Linear Classifiers to neural nets. From Boosting to Support Vector Machines. From Hidden Markov Models to graph transformer networks to Graphical Models. From Vector Quantization to Unsupervised Learning. Model selection and ensemble methods methods from Bayesian regularization to bagging to pruning to structural risk minimization.
G22.3033-002: Machine Learning and Pattern Recognition (updated version of the Spring 2004 course). A Graduate course that introduces most major machine learning and pattern recognition methods: from Linear Classifiers to neural nets. From Boosting to Support Vector Machines. From Hidden Markov Models to graph transformer networks to Graphical Models. From Vector Quantization to Unsupervised Learning. Model selection and ensemble methods methods from Bayesian regularization to bagging to pruning to structural risk minimization.
G22.3033-014: Machine Learning and Pattern Recognition. A Graduate course that introduces most major machine learning and pattern recognition methods: from Linear Classifiers to neural nets. From Boosting to Support Vector Machines. From Hidden Markov Models to graph transformer networks to Graphical Models. From Vector Quantization to Unsupervised Learning. Model selection and ensemble methods methods from Bayesian regularization to bagging to pruning to structural risk minimization.
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