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Basics of Machine Learning

To develop an understanding of the different concepts of Machine Learning, generating specific algorithms and working on practical models.


OVERVIEW

To develop an understanding of the different concepts of Machine Learning, generating specific algorithms and working on practical models. To develop an understanding of the different concepts of Machine Learning, generating specific algorithms and working on practical models.




CURRICULUM

Objective:

Introduction

Topics:

  • Basic concepts
  • Supervised learning setup. LMS
  • Logistic regression, Perceptron, Exponential family
  • Practice session on what is taught

Objective:

Supervised Learning

Topics:

  • Generative learning algorithms.
  • Gaussian discriminant analysis
  • Naive Bayes
  • Support vector machines
  • Discussion Section: Vectorization. 

Objective:

Practice ML Advice

Topics:

  • Bias/variance trade-off
  • Model selection and feature selection
  • Evaluating and debugging learning Algorithms
  •  Practical advice on structuring ML project
  • Problem session on what is taught
  • Discussion Section: Convex Optimization
  • Ask to work on projects

Objective:


Topics:

  • NN architecture
  • Forward/Back propagations 14 Vectorization
  • Other optimization tricks
  • Discussion Section - Evaluation

Objective:

Unsupervised Learning

Topics:

  • Clustering. K-means
  • EM. Mixture of Gaussians
  • Factor analysis; PCA (Principal components analysis)
  • ICA (Independent components analysis)
  • Problem session on what is taught
  • Discussion: Deep Learning Methods
  • Project

Objective:

Reinforcement learning and control 

Topics:

  • MDPs. Bellman equations
  • Value iteration and policy iteration
  • Linear quadratic regulation LQR, LQG
  • Q-learning Value function
  • Approximation
  • Problem session on what is taught
  • Discussion: Deep Learning Platform
  • Generative Adversarial Networks
  • Adversarial machine learning


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01-Jun-18 09:00-12:00 APPLY NOW
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CERTIFICATION

To develop an understanding of the different concepts of Machine Learning, generating specific algorithms and working on practical models. To develop an understanding of the different concepts of Machine Learning, generating specific algorithms and working on practical models.


Course Fee : ₹ 40000

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REQUIRED TOOLS

Scikit Learn

Simple and efficient tools for data mining and data analysis

Mahout

For Creating Scalable Performant Machine Learning Applications

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