Supervised Machine Learning: Regression and Classification Course Syllabus

Overview (80-120 words) describing structure and time commitment.

Module 1: Introduction & Linear Regression with One Variable

Estimated time: 3 hours

• Course logistics and learning objectives
• Data representations and feature vectors
• Linear regression algorithm and cost function
• Gradient descent intuition and implementation

Module 2: Linear Regression with Multiple Variables

Estimated time: 4 hours

• Multivariate linear regression model
• Feature normalization and scaling
• Normal equation vs. gradient descent
• Polynomial regression for nonlinear patterns

Module 3: Logistic Regression & Regularization

Estimated time: 4 hours

• Classification with logistic regression
• Sigmoid function and decision boundaries
• Cost function adaptation for classification
• Regularization to prevent overfitting

Module 4: Neural Networks: Representation

Estimated time: 3 hours

• Biological and artificial neurons
• Network architectures and layers
• Forward propagation mechanism
• Activation functions (sigmoid, tanh, ReLU)

Module 5: Neural Networks: Learning

Estimated time: 4 hours

• Backpropagation algorithm
• Random initialization of weights
• Gradient checking for debugging
• Hyperparameter tuning basics

Module 6: Advice for Applying Machine Learning & Support Vector Machines

Estimated time: 5 hours

• Train/validation/test splits
• Bias–variance trade-off analysis
• Error analysis and model improvement
• Support vector machines and Gaussian kernels

Prerequisites

• Basic knowledge of linear algebra
• Familiarity with Octave/MATLAB programming
• High school-level calculus and probability

What You'll Be Able to Do After

• Implement linear and logistic regression models from scratch
• Apply regularization techniques to improve model generalization
• Train and debug neural networks for classification tasks
• Select appropriate ML algorithms based on problem type
• Evaluate models using cross-validation and error analysis