Deep Learning Fundamentals

This module provides a foundational introduction to Machine Learning concepts and experimental methodologies. Students will learn the basic principles of ML and gain hands-on experience with essential ML tools and protocols used in deep learning applications.

This module covers the core concepts of deep learning, including modular approaches to neural network design, the mathematics of backpropagation, and optimization techniques. Students will learn how deep learning models are structured and trained.

This module explores the architecture and applications of Convolutional Neural Networks (CNNs). Students will learn about different CNN architectures and their applications in image processing and computer vision tasks.

This module focuses on sequence-based deep learning models, particularly Recurrent Neural Networks. Students will learn about sequence-to-sequence models and their applications in natural language processing.

This module addresses the critical ethical considerations in machine learning, focusing on bias and discrimination. Students will learn about different types of fairness and methods to address bias in pre-processing, in-processing, and post-processing stages of ML development.