Focus in Robotics and Control

Relevant Coursework :

• Robotics Studio: designing, prototyping, 3D printing and assembling a bipedal robot that ​moves by rowing on it's arms. Then making use of Raspberrypi to control servomotors and python libraries like PyBullet to simulate the robot to understand it's underlying physics to develop its kinematics and making it move. 

• Introduction to Robotics: study of forward and inverse kinematics for robots with various DOFs, Dynamics & Control, Jacobians, MATLAB programming for solving kinematics

• Data Science: developed technical skills in Python and Jupyter Notebooks, delving into linear regressions, classifications, clustering, and reinforcement learning. The curriculum emphasized complex ML methods, including gradient descent, regularization, tree-based models, and neural networks, alongside statistical analysis and model optimization techniques. 

• Deep Learning & Neural Networks: in this advanced Neural Networks and Deep Learning course, I mastered a range of deep learning architectures, including CNNs and RNNs, focusing on their practical applications. The curriculum emphasized deep forward networks, backpropagation, optimization for deep learning, and regularization techniques. I also gained insights into working with modern CNN architectures and explored cutting-edge topics such as autoencoders, GANs, Transfer Learning, LSTM and variational autoencoders, equipping me with up-to-date knowledge in the rapidly evolving field of deep learning.