AI or DL for image Processing detailed syllabus for Geoinformatics Engineering (Geo) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the Geo 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 Geoinformatics Engineering 5th Sem scheme and its subjects, do visit Geo 5th Sem 2021 regulation scheme. For Professional Elective-III scheme and its subjects refer to Geo Professional Elective-III syllabus scheme. The detailed syllabus of ai or dl for image processing is as follows.
Course Objectives:
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Unit I
EXPLORATORY DATA ANALYSIS Inferential statistics – hypothesis testing – spectral divergence- spectral angle mapper – spectral correlation analysis – support vector machines- tree models – unsupervised learning – clusters -k-means- fuzzy concepts – possibilistic k means – training date sets- random forest classifier -measures of accuracy: RMS, correlation co efficient, ROC
Unit II
ARTIFICIAL INTELLIGENCE Foundation of AI and history of AI intelligent agents: Agents and Environments, the concept of rationality, the nature of environments, structure of agents, problem solving agents, problem formulation – AI problems
Unit III
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Unit IV
DEEP LEARNING CONCEPTS AND METHODS Cloud essentials -Git hub – Concepts- convolution- pooling – activation functions – tensors-normalisation- sampling- training – loss function- optimizer – inference – ensemble techniques -models with multiple sources- patch based mode vs. fully convolutional mode- Introduction to CNNs-Back Propagation Algorithm, Vanishing and Exploding Gradients Overfitting Evolution of CNN Architectures: AlexNet, ZFNet, VGG Net, InceptionNets, ResNets, DenseNets.
Unit V
APPLICATIONS OF CNN CNNs for Detection: Background of Object Detection, R-CNN, Fast R-CNN, Faster R-CNN, YOLO. CNNs for Segmentation: Types of Segmentation: Instance vs semantic segmentation. FCN, Seg-Net, U-Net, Mask-RCNN.
Course Outcomes:
•On completion of the course, the student is expected
- To provide Knowledge about exploratory data analysis
- To understand concept of Artificial Intelligence
- To understand about learning based classifiers
- To learn concepts and various methods of deep learning
- To learn about various applications of CNN
Text Books:
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Reference Books:
- Bratko, “Prolog: Programming for Artificial Intelligence”, Fourth edition, Addison Wesley Educational Publishers Inc., 2011.
- M. Tim Jones, “Artificial Intelligence: A Systems Approach(Computer Science)”, Jones and Bartlett Publishers, Inc.; First Edition, 2008
- Phil Kim, “Matlab Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence”, Apress, 2017.
- Ragav Venkatesan, Baoxin Li, “Convolutional Neural Networks in Visual Computing”, CRC Press, 2017.
For detailed syllabus of all the other subjects of Geoinformatics Engineering 5th Sem, visit Geo 5th Sem subject syllabuses for 2021 regulation.
For all Geoinformatics Engineering results, visit Anna University Geo all semester results direct link.