ML – Supervised Learning

Introductory concepts

• Gradient Descent algorithm

Universal approximation models

• Feedforward Neural Networks
• Decision Trees
• Support Vector Machines
• k-Nearest Neighbours

Regression models

Classification models

• Linear Regression
• Model based Nonlinear Regression

• Logistic Regression
• Naive Bayes

Time Series Forecasting models

Specialised models and topics

• ARIMA Models
• Time delayed Neural Networks

• Ensemble Learning
• Convolutional Neural Networks
• Classification and Regression Metrics

Feature selection and extraction

• Feature Selection Methods

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