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Certificates from open course platform

Probabilistic Graphical Models 1: Representation

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly. https://www.coursera.org/account/accomplishments/certificate/46J299HZY7MS

UCSanDiego CSE167x: Computer Graphics

In this course, you will be learning the theory of computer graphics, as well as implementing practical systems for both real-time and offline graphics that utilize this knowledge. The course consists of four units, each with lecture videos and a programming assignment due every week or once every two weeks. By the end, you would have implemented a real-time scene viewer in OpenGL and GLSL, as well as a full raytracer to create realistic images of 3D scenes.

https://courses.edx.org/certificates/1f8aac67481f45fdbcd86dad2fbde5dc

Coursera deeplearning.ai specialization

by Andrew Ng, Head Teaching Assistant - Kian Katanforoosh & Teaching Assistant - Younes Bensouda Mourri deeplearning.ai

Neural Networks and Deep Learning

[https://www.coursera.org/account/accomplishments/certificate/8X9SLVGPW8WK]

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

[https://www.coursera.org/account/accomplishments/certificate/Z355LYR9AQZA]

Convolutional Neural Networks

[https://www.coursera.org/account/accomplishments/certificate/N23GGUQLRTNS]

Sequence Models

[https://www.coursera.org/account/accomplishments/certificate/BB7KZNKYF9V6]