CEI357: Deep and Reinforcement Learning syllabus for EIE 2021 regulation (Professional Elective-VII)

Deep and Reinforcement Learning detailed syllabus for Electronics & Instrumentation Engineering (EIE) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the EIE students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Electronics & Instrumentation Engineering 6th Sem scheme and its subjects, do visit EIE 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to EIE Professional Elective-VII syllabus scheme. The detailed syllabus of deep and reinforcement learning is as follows.

Course Objectives:

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Unit I

Learning algorithms, Maximum likelihood estimation, Building machine learning algorithm, Neural Networks Multilayer Perceptron, Back-propagation algorithm and its variants Stochastic gradient decent, Curse of Dimensionality.

Unit II

Machine Learning Vs. Deep Learning, Representation Learning, Width Vs. Depth of Neural Networks, Activation Functions: RELU, LRELU, ERELU, Unsupervised Training of Neural Networks, Restricted Boltzmann Machines, Auto Encoders.

Unit III

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Unit IV

Recurrent Neural Networks, Bidirectional RNNs – Encoder-decoder sequence to sequence architechures – BPTT for training RNN, Long Short Term Memory Networks.

Unit V

Deep Belief networks – Boltzmann Machines – Deep Boltzmann Machine – Generative AdversialNetworks.

Skill Development Activities

(Group Seminar/Mini Project/Assignment/Content Preparation / Quiz/ Surprise Test / Solving GATE questions/ etc)

  1. Fundamentals of machine learning
  2. Fundamentals of deep learning
  3. Realization and understanding of CNN
  4. Time series forecasting for data
  5. Generating of synthetic images

Course Outcomes:

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Text Books:

  1. Ian Goodfellow, Yoshua Bengio and Aaron Courville, � Deep Learning�, MIT Press, 2017.
  2. Josh Patterson, Adam Gibson “Deep Learning: A Practitioner’s Approach”, O’Reilly Media, 2017.

Reference Books:

  1. Umberto Michelucci �Applied Deep Learning. A Case-based Approach to Understanding Deep Neural Networks� Apress, 2018.
  2. Kevin P. Murphy “Machine Learning: A Probabilistic Perspective”, The MIT Press, 2012.
  3. Ethem Alpaydin,”Introduction to Machine Learning�, MIT Press, Prentice Hall of India, Third Edition 2014.
  4. Rezaul Karim, Ahmed Menshawy “Deep Learning with TensorFlow: Explore neural networks with Python”, Packt Publisher, 2017.

List of Open Source Software/ Learning Website:

  1. https://www.techtarget.com/searchenterpriseai/definition/machine-learning-ML
  2. https://www.techtarget.com/searchenterpriseai/definition/deep-learning-deep-neural-network
  3. https://www.simplilearn.com/tutorials/deep-learning-tutorial/rnn
  4. https://machinelearningmastery.com/what-are-generative-adversarial-networks-gans/

For detailed syllabus of all the other subjects of Electronics & Instrumentation Engineering 6th Sem, visit EIE 6th Sem subject syllabuses for 2021 regulation.

For all Electronics & Instrumentation Engineering results, visit Anna University EIE all semester results direct link.

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