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Artificial Intelligence
Problem Solving
 Solving Problems by Searching
 Beyond Classical Search
 Adversarial Search
 Constraint Satisfaction Problems
Knowledge and Reasoning
 Logical Agents
 First-Order Logic
 Inference in First-Order Logic
 Classical Planning
 Planning and Acting in the Real World
 Knowledge Representation
Uncertain Knowledge and Reasoning
 Quantifying Uncertainty
 Probabilistic Reasoning
 Probabilistic Reasoning over Time
 Making Simple Decisions
 Making Complex Decisions
Learning
 Learning from Examples
 Knowledge in Learning
 Learning Probabilistic Models
 Reinforcement Learning

Machine Learning

Introduction and ANN Structure
 Biological neurons and artificial neurons
 Model of an ANN
 Activation functions used in ANNs
 Typical classes of network architectures
Mathematical Foundations and Learning mechanisms
 Re-visiting vector and matrix algebra
 State-space concepts
 Concepts of optimization
 Error-correction learning
 Memory-based learning
 Hebbian learning
 Competitive learning
Single layer perceptrons
 Structure and learning of perceptrons
 Pattern classifier – introduction and Bayes' classifiers
 Perceptron as a pattern classifier
 Perceptron convergence
 Limitations of a perceptrons
Feedforward ANN
 Structures of Multi-layer feedforward networks
 Back propagation algorithm
 Back propagation – training and convergence
 Functional approximation with back propagation
 Practical and design issues of back propagation learning

Competitive Learning and Self organizing ANN.
 General clustering procedures

 Self organizing feature maps
 Properties of feature maps
Fuzzy Neural Networks
 Neuro-fuzzy systems
 Background of fuzzy sets and logic
 Design of fuzzy stems
 Design of fuzzy ANNs
MACHINE LEARNING
 Bias – Variance tradeoff
 Regularisation
 Over-fitting
 Support Vector Machines
 Kernel Trick
 PCA and Kernel PCA
 K Means Clustering
 Self-Organization Maps (SOM)
 Kernel induced vector space
 Mercer Kernels and Kernel – induced similarity metrics
 Reinforcement Learning
DEEP LEARNING
This will be taught in relation to above topics covered.
 Logistic and Softmax Regression
 Sparse Autoencoders
 Vectorization, PCA and Whitening
 Self-Taught Learning
 Deep Networks
 Linear Decoders
 Convolution and Pooling
 Sparse Coding
 Independent Component Analysis

 Canonical Correlation Analysis
 Demos and Applications

Applications & Projects using Python Libraries
 A few examples of Neural Network applications, their advantages and problems will be
discussed
 OR Logic, AND Logic & XOR Logic Example using ANN
 Housing Prizes Prediction
 Single Line Hypothesis Training
 Share Market Prediction
 Marks Prediction
 Cancer Detection
 Character Recognition using SVM
 Automatic Product Classification & Clustering based on Retails Context
 Predictive Analysis based on Business & Housing values
 Using Datasets available on UCI, github and other opensource platforms

Fee : INR 9899/- ( Includes  Study Material , Codes and Documentation , Certification) .

Certification

  1. Certification from Techniche IIT Guwahati.
  2. International Certification from Robotech Labs Private Limited
  3. Internship Certificate with Project Letter

Centers

  • Noida – Robotech Labs , E 220 Sector 63 Noida
  • Chennai – IIT Madras Research Park
  • Mumbai – To be Announced
  • Hyderabad – To be Announced

Dates :

Chennai  :

June 11- June 17th 2018

Noida :

Batch 1 : May 14th      Batch 2 : June 4th

Batch 3: June 18th       Batch 4 : July 2nd

Mumbai – July 2018

Hyderbad – May 2018

 

How To Enroll

  1. Fill the Registration Form.
  2. Pay the registration fee. Pay Fees Now by Clicking this Link.
  3. Its done, the balance fee to be paid later
  4. Only 20 seats in a batch.

Accommodation

We will share with you the details of the PG’s and Guest House available in the vicinity. You can choose to book the PG/Hostel beforehand according to the facilities you need and your budget.

 

Summer Internship 2018

Register here


One Comment

  • Rutvik says:

    Please send the following information for 30day and 45days………………….
    (i)details of coverage & deliverables
    (ii) timings
    (iii) fees & other expenses for supplies

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