Machine Learning for Intelligent Systems detailed syllabus for Mechanical Engineering Sandwich (Mech Sandwich) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the Mech Sandwich 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 Mechanical Engineering Sandwich 8th Sem scheme and its subjects, do visit Mech Sandwich 8th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to Mech Sandwich Professional Elective-VII syllabus scheme. The detailed syllabus of machine learning for intelligent systems is as follows.
Course Objectives:
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Unit I
INTRODUCTION TO MACHINE LEARNING 9
Philosophy of learning in computers, Overview of different forms of learning, Classifications vs. Regression, Evaluation metrics and loss functions in Classification, Evaluation metrics and loss functions in Regression, Applications of AI in Robotics.
Unit II
CLUSTERING AND SEGMENTATION METHODS 9
Introduction to clustering, Types of Clustering, Agglomerative clustering, K-means clustering, Mean Shift clustering, K-means clustering application study, Introduction to recognition, K-nearest neighbor algorithm, KNN Application case study, Principal component analysis (PCA), PCA Application case study in Feature Selection for Robot Guidance.
Unit III
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Unit IV
NEURAL NETWORKS 9
Mathematical Models of Neurons, ANN architecture, Learning rules, Multi-layer Perceptrons, Back propagation, Introduction of Neuro-Fuzzy Systems, Architecture of Neuro Fuzzy Networks, Application Case Study of Neural Networks in Robotics
Unit V
RNN AND REINFORCEMENT LEARNING 9
Unfolding Computational Graphs, Recurrent neural networks, Application Case Study of recurrent networks in Robotics, Reinforcement learning, Examples for reinforcement learning, Markov decision process, Major components of RL, Q-learning. Application Case Study of reinforcement learning in Robotics
Course Outcomes:
At the end of the course the students would be able to
- Understand basic machine learning techniques such as regression, classification
- Understand about clustering and segmentation
- Model a fuzzy logic system with fuzzification and defuzzification
- Understand the concepts of neural networks and neuro fuzzy networks.
- Gain knowledge on Reinforcement learning.
Text Books:
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Reference Books:
- Bruno Siciliano, Oussama Khatib, “Handbook of Robotics”, 2016 2nd Edition, Springer
- Simon Haykin, “Neural Networks and Learning Machines: A Comprehensive Foundation”, Third Edition, Pearson, delhi 2016.
- Timothy J Ross, “Fuzzy Logic with Engineering Applications”, 4th Edition, Chichester, 2011, Sussex Wiley.
For detailed syllabus of all the other subjects of Mechanical Engineering Sandwich 8th Sem, visit Mech Sandwich 8th Sem subject syllabuses for 2021 regulation.
For all Mechanical Engineering Sandwich results, visit Anna University Mech Sandwich all semester results direct link.