Kursinformation

At the end of this course, students should be able to use Git and write efficient Python code to analyze and visualize data using recent ML techniques such as Deep Neural Networks, PCA, k-means, Reinforcement learning, Manifold learning, etc... This course aims to provide the students with the algorithmic concepts, the approaches to translating their algorithms into Python code and finally, how to optimize their code or find the appropriate Python library or module adapted to their needs. Several Python modules will be introduced in this course, such as NumPy, Scipy, Pandas, Matplolib, Scitiklearn, Tensorflow and more. And to allow students to practice during the course constantly, individual homework will be given, and group projects along this course. The sum of students' effort in contributing to the lecture, group projects and homework will yield their overall marks.