ADC604

Mumbai University

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Summary

Linear Models
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13

September 2025

2024/2025
The linear models section of machine learning focuses on techniques for regression and classification. It covers the least-squares method, multivariate linear regression, and regularized regression, including the use of least-squares regression for classification tasks. Additionally, it introduces Support Vector Machines (SVMs) as a powerful classification method, emphasizing their role in finding optimal decision boundaries for separating data classes.
samithakur
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Summary

Mathematical Foundation for ML
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1

September 2025

2025/2026
The mathematical foundation for machine learning covers essential linear algebra concepts needed to understand and implement ML algorithms. It includes systems of linear equations, norms, inner products, vector length, distances between vectors, and orthogonality of vectors. It also addresses properties of symmetric positive definite matrices, determinants, trace, eigenvalues and eigenvectors, orthogonal projections, and diagonalization. Additionally, Singular Value Decomposition (SVD) and its a...
samithakur
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Summary

Machine Learning Introduction
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19

September 2025

2024/2025
The syllabus covers the fundamentals and applications of machine learning, starting with an introduction to the field, common issues, and real-world applications. It explains the steps involved in developing a machine learning application and distinguishes between supervised learning, including classification and prediction, and unsupervised learning, such as clustering. Key concepts in model evaluation, including training, testing, validation datasets, cross-validation, overfitting, and underfi...
samithakur
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