Workshop on Statistical and Machine Learning Techniques 2024

OBJECTIVE
This workshop will help the participants understand principles of machine learning and how statistics is crucial while developing machine learning models. At the end of this workshop, participants will be able to get an understanding about how statistical machine learning can help in making better/informed decisions and deploy ML models.. The aim of this workshop is integrating machine learning into academic research can facilitate interdisciplinary collaborations, broaden the scope of inquiries, and contribute to pushing the frontiers of knowledge.
COURSE CONTENTS
Introduction to Machine Learning
• Introduction to machine learning: supervised vs. unsupervised learning
• Linear models (including linear regression and logistic regression)
• Loss functions and optimization Model Selection
• Cross-validation
• The bias-variance tradeoff
• High-dimensional variable selection
• Regularization methods (lasso and ridge regression) Non-linear Methods & Deep Learning
• Support vector machines
• Decision trees
• Random forests
• Neural networks
• Deep learning

OBJECTIVE
This workshop will help the participants understand principles of machine learning and how statistics is crucial while developing machine learning models. At the end of this workshop, participants will be able to get an understanding about how statistical machine learning can help in making better/informed decisions and deploy ML models.. The aim of this workshop is integrating machine learning into academic research can facilitate interdisciplinary collaborations, broaden the scope of inquiries, and contribute to pushing the frontiers of knowledge.
COURSE CONTENTS
Introduction to Machine Learning
• Introduction to machine learning: supervised vs. unsupervised learning
• Linear models (including linear regression and logistic regression)
• Loss functions and optimization Model Selection
• Cross-validation
• The bias-variance tradeoff
• High-dimensional variable selection
• Regularization methods (lasso and ridge regression) Non-linear Methods & Deep Learning
• Support vector machines
• Decision trees
• Random forests
• Neural networks
• Deep learning

OBJECTIVE
This workshop will help the participants understand principles of machine learning and how statistics is crucial while developing machine learning models. At the end of this workshop, participants will be able to get an understanding about how statistical machine learning can help in making better/informed decisions and deploy ML models.. The aim of this workshop is integrating machine learning into academic research can facilitate interdisciplinary collaborations, broaden the scope of inquiries, and contribute to pushing the frontiers of knowledge.
COURSE CONTENTS
Introduction to Machine Learning
• Introduction to machine learning: supervised vs. unsupervised learning
• Linear models (including linear regression and logistic regression)
• Loss functions and optimization Model Selection
• Cross-validation
• The bias-variance tradeoff
• High-dimensional variable selection
• Regularization methods (lasso and ridge regression) Non-linear Methods & Deep Learning
• Support vector machines
• Decision trees
• Random forests
• Neural networks
• Deep learning

Last date for receipt of Applications : 21.05.2024
Intimation of Selection : 23.05.2024
Confirmation by participants : 27.05.2024

Registration Fee Rs.550/- +18% GST (Faculty & Students)
• The registration amount (Rs.649) send through GPAY No: 9788654804

Perundurai, Erode-638 052, Tamilnadu, India

Accommodation and boarding if required will be provided in the college campus on chargeable basis.