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    Quick Facts

    Medium Of InstructionsMode Of LearningMode Of Delivery
    EnglishSelf Study, Virtual ClassroomVideo and Text Based

    Courses and Certificate Fees

    Fees InformationsCertificate AvailabilityCertificate Providing Authority
    INR 25183yesMIT Cambridge

    The Syllabus

    • Introduction
    • Linear classifiers, separability, perceptron algorithm
    • Maximum margin hyperplane, loss, regularization
    • Stochastic gradient descent, over-fitting, generalization
    • Linear regression
    • Recommender problems, collaborative filtering
    • Non-linear classification, kernels
    • Learning features, Neural networks
    • Deep learning, back propagation
    • Recurrent neural networks
    • Recurrent neural networks
    • Generalization, complexity, VC-dimension
    • Unsupervised learning: clustering
    • Generative models, mixtures
    • Mixtures and the EM algorithm
    • Learning to control: Reinforcement learning
    • Reinforcement learning continued
    • Applications: Natural Language Processing

    • Automatic Review Analyzer
    • Digit Recognition with Neural Networks
    • Reinforcement Learning

    Instructors

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