Machine Learning

Objectives

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.

 

General characterization

Code

200179

Credits

7.5

Responsible teacher

Roberto André Pereira Henriques

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

Available soon

Bibliography

Teaching method

Evaluation method

Subject matter