Introduction to Robotics

Course Description

This course introduces the principles and applications of robotics, with a focus on the design and control of robotic systems. Topics covered include robot kinematics and dynamics, sensors and perception, motion planning, and intelligent control. The course includes both lectures and hands-on lab sessions, in which students will have the opportunity to program and experiment with real robotic systems.

Learning Outcomes

  • Understand the fundamental concepts and principles of robotics
  • Be able to design and implement simple control algorithms for robotic systems
  • Understand the role of sensors and perception in robotic systems
  • Become familiar with current and emerging applications of robotics

Textbook

Introduction to Robotics: Mechanics and Control (3rd Edition) by John J. Craig

Introduction to Neural Networks

Course Description

This course introduces the fundamental concepts and principles of neural networks, with a focus on deep learning techniques. Topics covered include the architecture and training of neural networks, supervised and unsupervised learning, and applications of neural networks in areas such as computer vision, natural language processing, and robotics. The course includes both lectures and hands-on lab sessions, in which students will have the opportunity to implement and experiment with neural network models using popular deep learning frameworks such as TensorFlow and PyTorch.

Learning Outcomes

  • Understand the fundamental concepts and principles of neural networks
  • Be able to design, implement, and train simple neural network models
  • Understand the basics of supervised and unsupervised learning in neural networks
  • Become familiar with current and emerging applications of neural networks

Textbook

Deep Learning (2nd Edition) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville