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This course is for upper-level graduates who are planning careers in computational neuroscience. It focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


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Course Credit: MIT

Course Curriculum

Module 01
The Course at a Glance 02:00:00
The Learning Problem in Perspective 01:15:00
Reproducing Kernel Hilbert Spaces 01:15:00
Regression and Least-Squares Classification 00:45:00
Support Vector Machines for Classification 01:15:00
Manifold Regularization 01:00:00
Unsupervised Learning Techniques 01:00:00
Multiclass 01:30:00
Ranking 00:45:00
Boosting and Bagging 00:30:00
Module 02
Online Learning 01:00:00
Generalization Bounds 00:45:00
Stability of Tikhonov Regularization 00:45:00
Uniform Convergence Over Function Classes 01:00:00
Uniform Convergence for Classification 00:45:00
Neuroscience 00:30:00
Active Learning 01:30:00
Math Camp 1: Functional Analysis 01:30:00
Math Camp 2: Probability Theory 00:15:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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