About The Course
The course explores basics of machine learning and the theory behind optimization. Introduces gradient descent, backpropagation and variants. Studies dense networks, MNIST classification, deep networks, convolutional neural networks, recurrent neural networks, long short-term memory networks, autoencoders and variational autoencoders. Project involved research on contemporary advancements in the field, and the design of a machine learning algorithm to solve a real-world problem using Python.
Course Instructor: Dr. Usman Tariq
My Course Grade: A
Documents
Course Syllabus:
My Course Project:
Progress Report 1:
Progress Report 2:
Final Report:
Taha Ameen 