Deep Learning
This course introduces deep learning techniques and their applications in AI-driven tasks.
Core Machine Learning Concepts: Covers datasets, evaluation, overfitting, and regularization.
Neural Networks: Explores linear/logistic regression, shallow neural networks, and deep learning architectures.
Implementation: Includes algorithm development and hands-on use of machine learning libraries.
Applications: Focuses on image classification, speech recognition, and natural language processing.
Project & Research: Concludes with student-led projects and conference-style paper presentations.
Students gain practical expertise in deep learning model development and real-world AI applications.
G1: Translate mechatronics systems and robots into mathematical models and simulations.
G2: Critically evaluate robotic systems in comparison with state-of-the-art robotic research.
G4: Develop, test, and integrate robotics hardware and software into fully functional systems.
G5: Design, develop, and interface Embedded Systems, ensuring efficient coding and integration.