Artificial Intelligence and Machine Learning
In our age of burgeoning smart technology and automation we are already seeing the transformative potential of Artificial Intelligence and Machine Learning in fields as diverse as finance, medicine, and manufacturing. This course offers a hands-on introduction to this future-focused area of research.
Practical information:
Georganiseerd door:
You will be introduced to the Python programming language and the theoretical foundations of the key concepts in Artificial Intelligence and Machine Learning, before embarking on linear regressions and tackling loss function, regularization techniques, and bias-variance trade-off. You will explore and implement stochastic gradients descent for regression using TensorFlow and PyTorch. The course progresses from simple Neural Networks to Convolutional Neural Networks and the implementation of MNIST classification. By the end of the course large-scale problems of semantic segmentation, edge detection and metric learning will be implemented on AWS/Google Cloud. Throughout the course you will solve practical problems of Artificial Intelligence and Machine Learning from diverse domains.
After studying this course you will:
- Understand the theory of machine learning and artificial intelligence.
- Know about Artificial Intelligence and Machine Learning tools used in practice.
- Know how to implement basic algorithms of Artificial Intelligence and Machine Learning and train small networks for practical problems.
- Be able to identify and use relevant Artificial Intelligence and Machine Learning tools in their research.
- Know how to implement and deploy Artificial Intelligence and Machine Learning algorithms on AWS/Google Cloud.