UCLA ELECTRICAL ENGINEERING
Do you want to learn about the foundations of Machine Learning and Inference from Data?
EE210A Adaptation and Learning
(Instructor: A. H. Sayed, Winter 2017)
In this course, students learn to master tools, algorithms, and core concepts related to machine learning, inference from data, data analysis, and adaptation and learning theories.
Description of the course
The course covers the fundamentals of inference and learning from data, with emphasis on online adaptation and learning mechanisms. Students learn about the foundations of adaptive and machine learning techniques in a unified and insightful treatment. In particular, the course covers topics related to optimal inference, stochastic-gradient algorithms for optimization, adaptation, and learning, Bayes and naive classifiers, nearest-neighbor rules, self-organizing maps, decision trees, logistic regression, discriminant analysis, Perceptron, support vector machines, kernel methods, bagging, boosting, random forests, cross-validation, principal component analysis. Design experiments and projects usually selected from topics related to channel estimation and equalization, echo cancellation, SVM learning machines, boosting and cross validation, discriminant analysis, neural networks, deep learning, convolutional networks, and reinforcement learning.
Background students will need
It is beneficial that students have some familiarity with matrix theory, linear algebra, and random variables. Supplemental material on these topics is provided and reviewed by the instructor.
Do you want to learn about the foundations of Machine Learning and Inference from Data?
EE210A Adaptation and Learning
(Instructor: A. H. Sayed, Winter 2017)
In this course, students learn to master tools, algorithms, and core concepts related to machine learning, inference from data, data analysis, and adaptation and learning theories.
Description of the course
The course covers the fundamentals of inference and learning from data, with emphasis on online adaptation and learning mechanisms. Students learn about the foundations of adaptive and machine learning techniques in a unified and insightful treatment. In particular, the course covers topics related to optimal inference, stochastic-gradient algorithms for optimization, adaptation, and learning, Bayes and naive classifiers, nearest-neighbor rules, self-organizing maps, decision trees, logistic regression, discriminant analysis, Perceptron, support vector machines, kernel methods, bagging, boosting, random forests, cross-validation, principal component analysis. Design experiments and projects usually selected from topics related to channel estimation and equalization, echo cancellation, SVM learning machines, boosting and cross validation, discriminant analysis, neural networks, deep learning, convolutional networks, and reinforcement learning.
Background students will need
It is beneficial that students have some familiarity with matrix theory, linear algebra, and random variables. Supplemental material on these topics is provided and reviewed by the instructor.