I have similar experience to you (very new to computer vision), but here's a few ideas off the top of my head: Some form of 3D mapping for autonomous vehicles like seen in this video. It uses a cool technique called SLAM. This is probably way beyond either of our scope but if you want a large project to work towards, this is an idea. An objectively easier idea may be implementing already established techniques such as object recognition and/or tracking (traffic signs or people in a picture/video), nothing is wrong with working on already solved problems imo. Also consider checking out the AWS DeepRacer scholarship challenge, maybe by participating you can figure something out.

While machine learning today is dominated by deep neural network research, in the 1990s neural approaches were not recognized as reliable for real-world applications. Back then, researchers put their efforts into kernel methods and support vector machines (SVM). One of the most notable and respected contributors to kernel methods and SVM is John Shawe-Taylor, a professor at University College London (UK) and Director of the Centre for Computational Statistics and Machine Learning (CSML). His main research area is Statistical Learning Theory, but his contributions range from neural networks to machine learning and graph theory. Shawe-Taylor has published over 300 papers with over 42000 citations.

Springboard runs an online, self-paced, Machine Learning Engineering Career Track in which participants learn with the help of a curated curriculum and 1-1 guidance from an expert mentor. Our mentor community – the biggest strength of our programs – comprises experts from the best organizations in the world. Our mentors range from engineers and researchers at premier companies (Netflix, Pandora, LinkedIn, Apple) to a wide variety of top-notch startups and research institutes. If you are as passionate about mentoring as you are about machine learning, and can give a few hours per week in return for an honorarium, we would love to hear from you. This 6-month course is primarily designed for Software Engineers who want to become Machine Learning Engineers.

Zhou Yi was terrible at math. He risked never getting into college. Then a company called Squirrel AI came to his middle school in Hangzhou, China, promising personalized tutoring. He had tried tutoring services before, but this one was different: instead of a human teacher, an AI algorithm would curate his lessons. The 13-year-old decided to give it a try. By the end of the semester, his test scores had risen from 50% to 62.5%. Two years later, he scored an 85% on his final middle school exam. "I used to think math was terrifying," he says. "But through tutoring, I realized it really isn't that hard. It helped me take the first step down a different path."

As AI is becoming more and smarter, many professionals are wondering is their Job is Safe or Not. When we look, into our jobs that are within it. We are so much thrilled to know about marketing managers. That which have only 1.4% chance of getting more Jobs. That is being Automated or they were replaced by artificial Intelligence.

Attempts from different disciplines to provide a fundamental understanding of deep learning have advanced rapidly in recent years, yet a unified framework remains relatively limited. In this article, we provide one possible way to align existing branches of deep learning theory through the lens of dynamical system and optimal control. By viewing deep neural networks as discrete-time nonlinear dynamical systems, we can analyze how information propagates through layers using mean field theory. When optimization algorithms are further recast as controllers, the ultimate goal of training processes can be formulated as an optimal control problem. In addition, we can reveal convergence and generalization properties by studying the stochastic dynamics of optimization algorithms. This viewpoint features a wide range of theoretical study from information bottleneck to statistical physics. It also provides a principled way for hyper-parameter tuning when optimal control theory is introduced. Our framework fits nicely with supervised learning and can be extended to other learning problems, such as Bayesian learning, adversarial training, and specific forms of meta learning, without efforts. The review aims to shed lights on the importance of dynamics and optimal control when developing deep learning theory.

Automatic Machine Learning (Auto-ML) has attracted more and more attention in recent years, our work is to solve the problem of data drift, which means that the distribution of data will gradually change with the acquisition process, resulting in a worse performance of the auto-ML model. We construct our model based on GBDT, Incremental learning and full learning are used to handle with drift problem. Experiments show that our method performs well on the five data sets. Which shows that our method can effectively solve the problem of data drift and has robust performance.

While some education experts applaud the advancement of high-tech grammar tools as a way to help people more clearly express their thoughts, others aren't so sure. Artificial intelligence, according to the contrarians, is only as smart as the humans who program it, and often just as biased. "Language is part of your heritage and identity, and if you're using a tool that is constantly telling you, 'You're wrong,' that is not a good thing," said Paulo Blikstein, associate professor of communications, media and learning technology design at Columbia University Teachers College. "There is not one mythical, monolithical (English) … And every time we have tried to curtail the evolution of a language, it has never gone well." In the era of spellcheck and auto-correct, does it matter that my son can't spell?

It's no secret that our cybersecurity industry today suffers from a yawning talent gap -- a statistical juggernauton track to reach 3.5 million unfilled positions by 2021. As the wakeup call spreads, we're seeing more cross-disciplinary trainingsand nurture efforts deep into the educational pipeline -- from pre-K, elementaryand middle schoolinitiatives, to programs for high schooland higher education. We just got fresh momentum from the recent White House "Executive Order on America's Cybersecurity Workforce," which proclaims our collective cybersecurity talent pool "a strategic asset that protects the American people" and in need of "work-based learning, apprenticeships, and blended learning approaches…for both new workforce entrants and those who are advanced in their careers." As a cybersecurity education evangelist, this proclamation is at once music to my ears and the mother of all to-do lists. That's because the Executive Order focuses primarily on the Why and What -- meaning it's largely up to the industry to keep figuring out the How.

Adoption of artificial intelligence is on the rise: According to research firm Gartner, 37 percent of organizations have now "implemented AI in some form," and adoption is up 270 percent over the past four years. Schools are following suit: Technavio's "Artificial Intelligence Market in the US Education Sector 2018-2022" report predicts a nearly 48 percent growth rate for AI tools over the next three years. As noted by MIT Technology Review, the rapid development and uptake of AI solutions has created an environment where companies may "obfuscate and oversell" AI abilities even as organizations race to implement new solutions and keep up with the competition. The key to AI success is specificity. It is crucial to define key needs AI tools can meet and shortcomings it can address.