Aprendizagem Automática

Objetivos

Machine learning is revolutionizing numerous areas by enabling computers to learn from data and make intelligent predictions and decisions. In this course, students will gain a strong foundation in the core concepts, applications, and tools for machine learning. Through theory and hands-on programming, they will develop skills to build and deploy machine-learning models for real-world tasks.

The general Learning Objectives are:

• Understand the significant machine learning approaches, including supervised learning and unsupervised learning.
• Use Python to implement popular machine learning algorithms like linear regression, logistic regression, neural networks, support vector machines, decision trees, and k-means clustering.
• Evaluate and compare machine learning models using proper evaluation metrics and techniques like train/test splits, cross-validation, confusion matrices, and classification reports.
• Gain experience with the whole machine learning workflow, including data exploration, data cleaning and preprocessing, feature engineering, model optimization, and deployment.
• Apply machine learning to solve real-world problems through hands-on projects and assignments using datasets from domains like computer vision, natural language processing, and recommender systems.
• Develop proper techniques to avoid overfitting, handle missing data, and perform feature selection and dimensionality reduction.

By completing this course, students will gain valued machine-learning skills to drive innovations and technologies powered by artificial intelligence.