G22.2565001: Machine Learning and Pattern Recognition.
Graduate course.
V22.0480001:
Introduction to Robotics. A handson undergraduate course on
robotics and embedded systems.
Machine Learning and Pattern Recognition. Graduate course
V22.0480001:
Introduction to Robotics. A handson 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.
G22.2565001:
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.
G22.2565001:
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.
G22.3033003:
Mobile Robotics. A seminar/workshop course on mobile robotics.
V22.0480002:
Introduction to Machine Learning and Pattern Recognition. An
undergraduate Senior/Juniorlevel 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.
G22.2565001:
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.
G22.3033007:
Mobile Robotics. A PhDlevel seminar/workshop course
on mobile robotics.
G22.3033013:
Advanced Machine Learning Seminar.
A seminarstyle course on advanced topics in Machine Learning.
G22.2565001:
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.
V22.0480006:
Introduction to Machine Learning and Pattern Recognition. An
undergraduate Senior/Juniorlevel 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.
G22.3033002: 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.
G22.3033014: 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.
