ApL405: Difference between revisions
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| credit_structure = 2-0-2 | | credit_structure = 2-0-2 | ||
| pre_requisites = | | pre_requisites = | ||
| overlaps = ELL784, COL341, COL774 | | overlaps = [[ELL784]], [[COL341]], [[COL774]] | ||
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== ApL405 : Machine Learning in Mechanics == | == ApL405 : Machine Learning in Mechanics == | ||
Introduction: Linear Algebra, Probability review, Programming Basics, Challenges in Data Handling Regression: Simple Linear Regression, Multiple Linear Regression, Nonlinear Regression, Logistic regression Introduction to Machine learning: Supervised Learning, Unsupervised Learning, Classification and Clustering Algorithms Applications of Machine Learning in Mechanics: Case Studies include Identifying faulty/healthy wind turbines, Turbulent Flow Analysis, Courses of Study 2024-2025 Applied Mechanics114Leakage Detection in Hydraulic Circuits, Fault Detection in Motor- Bearings, Human Activity Recognition, Heart Sound Classification etc. Deep learning: Introduction to Neural Networks, Convolution and Artificial Neural Networks, Applications in Engineering Mechanics Practical's: MATLAB tools including Curve Fitting Toolbox, Classification Learner App, Deep Network Designer App, Tensor Flow, Training models on GPUs. | Introduction: Linear Algebra, Probability review, Programming Basics, Challenges in Data Handling Regression: Simple Linear Regression, Multiple Linear Regression, Nonlinear Regression, Logistic regression Introduction to Machine learning: Supervised Learning, Unsupervised Learning, Classification and Clustering Algorithms Applications of Machine Learning in Mechanics: Case Studies include Identifying faulty/healthy wind turbines, Turbulent Flow Analysis, Courses of Study 2024-2025 Applied Mechanics114Leakage Detection in Hydraulic Circuits, Fault Detection in Motor- Bearings, Human Activity Recognition, Heart Sound Classification etc. Deep learning: Introduction to Neural Networks, Convolution and Artificial Neural Networks, Applications in Engineering Mechanics Practical's: MATLAB tools including Curve Fitting Toolbox, Classification Learner App, Deep Network Designer App, Tensor Flow, Training models on GPUs. | ||
Latest revision as of 16:22, 14 April 2026
| ApL405 | |
|---|---|
| Machine Learning in Mechanics | |
| Credits | 3 |
| Structure | 2-0-2 |
| Pre-requisites | |
| Overlaps | ELL784, COL341, COL774 |
ApL405 : Machine Learning in Mechanics
Introduction: Linear Algebra, Probability review, Programming Basics, Challenges in Data Handling Regression: Simple Linear Regression, Multiple Linear Regression, Nonlinear Regression, Logistic regression Introduction to Machine learning: Supervised Learning, Unsupervised Learning, Classification and Clustering Algorithms Applications of Machine Learning in Mechanics: Case Studies include Identifying faulty/healthy wind turbines, Turbulent Flow Analysis, Courses of Study 2024-2025 Applied Mechanics114Leakage Detection in Hydraulic Circuits, Fault Detection in Motor- Bearings, Human Activity Recognition, Heart Sound Classification etc. Deep learning: Introduction to Neural Networks, Convolution and Artificial Neural Networks, Applications in Engineering Mechanics Practical's: MATLAB tools including Curve Fitting Toolbox, Classification Learner App, Deep Network Designer App, Tensor Flow, Training models on GPUs.