Course Interaction: Every Thursday (L10) 12:00-13:15

*Course Contents and Lecture Wise Schedule*

Topic |
Details |
Lecture Hours |

Feed-forward Networks |
Simple Neuron Model, Multi-layered Networks, Back-propagation Algorithm, Generalized delta rule, Radial basis function network, Adaptive Learning rate, Examples |
8 |

Feedback Networks |
Back-propagation through time, real-time recurrent learning, LSTM |
6 |

Self-organizing Networks |
Unsupervised Learning, Kohonen SOM, Extended Kohonen SOM |
3 |

Application I |
Visual Motor Coordination - learning to manipulate |
3 |

Application II |
Indirect and Direct Adaptive Control - learning that guarantees stability |
5 |

Approximate Dynamic Programming |
Adaptive Critic networks and learning approaches |
6 |

Deep Learning |
RBM, CNN, Deep Reinforcement Learning, Auto-encoder based deep network |
12 |

**Evaluation Components**

Continuous Evaluation: 30%

Assignment Tests: 30%

Final Exam: 40%

**Text Book**

Laxmidhar Behera and I Kar, Intelligent Systems and Control, OUP, 2009, 5th Reprint

Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, www.deeplearningbook.org

**Reference Book**

Simon Haykin, Neural Networks - A Comprehensive Foundations, 1994