Course

Objective:

Learning Outcomes:

• Analyze and evaluate feature selection, scaling, and outlier detection methods to improve data quality and support robust machine learning model development
• Develop and apply supervised learning models for regression tasks, including linear and regularized approaches (Ridge, LASSO, Elastic Net), and assess model performance using appropriate error metrics
• Design and implement classification models—including logistic regression, decision trees, and ensemble methods—and evaluate their performance using suitable metrics and strategies for handling imbalanced datasets
• Apply and compare unsupervised learning techniques, including clustering and dimensionality reduction algorithms, to extract patterns and insights from unlabeled data
• Explain and implement foundational deep learning architectures and analyze their applications in solving complex supervised and unsupervised learning problems
• Describe and apply reinforcement learning concepts, including agent–environment interactions and reward-based optimization, to model sequential decision-making problems
• Integrate and demonstrate end-to-end machine learning workflows by building, evaluating, and communicating solutions (e.g., recommender systems) using industry-standard tools and collaborative practices
• Acquire, preprocess, and manage data from diverse sources (e.g., SQL, NoSQL, APIs, and cloud platforms) by applying data cleaning, feature engineering, and transformation techniques to prepare datasets for analysis

General Topics: