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
Programs
Programs where the course is taught: