Course overview
This course aims to equip students with a foundational understanding of machine learning concepts, their historical development, and their applications. By the end of the course, learners will be able to understand and implement basic machine learning algorithms and evaluate the algorithms on a variety of datasets using suitable evaluation metrics. This course prepares students to effectively apply machine learning techniques to real-world problems and sets the foundation for more advanced studies in machine learning and artificial intelligence.
- Deep Learning
- Neural Networks
- Transformer
Course learning outcomes
- Select a suitable neural network architecture for a given problem
- Implement neural network architectures in a modern framework
- Apply the machine learning pipeline to neural network models on complex datasets
- Evaluate different deep learning architectures for specific tasks
- Apply deep learning models for natural language processing and computer vision
- Present results and recommendations to stakeholders
Degree list
The following degrees include this course