The 2020 LHC Olympics challenged teams to develop a machine learning code to find a hidden signal in particle-collision data. This image shows particle-collision data captured by the ATLAS detector at CERN's Large Hadron Collider. In searching for new particles, physicists can lean on theoretical predictions that suggest some good places to look and some good ways to find them: It's like being handed a rough sketch of a needle hidden in a haystack. But blind searches are a lot more complicated, like hunting in a haystack without knowing what you are looking for. To find what conventional computer algorithms and scientists may overlook in the huge volume of data collected in particle collider experiments, the particle physics community is turning to machine learning, an application of artificial intelligence that can teach itself to improve its searching skills as it sifts through a haystack of data.

The way people receive and consume news and entertainment has changed drastically nowadays with features such as personalized content coming into play. Other technologies have also changed the entire ball game regarding content creation and distribution. Although this subset of AI seemed to thrive, the growth was quite stagnant in the education industry, but not of late. There are many applications of AI in the education industry that have transformed the perspective of many students by enabling smart learning. So, how has AI changed the education industry and what is the future in this?

It is sad that we are affected by Coronavirus outbreak which has led to schools/colleges being closed, engineering/mba internships being cancelled and job joining being postponed. But, on a positive side we can use this time of crisis to invest in ourselves to hone our skills and prepare for a better tomorrow. Having spent 6 years in the industry as a software developer/product manager and with a decade of experience in Python programming, I want to give back to the community in this time of crisis. I will share with you lectures, notebooks, have youtube/hangout live training workshops to teach you skills which can help you in the industry.

College students are enrolled at each semester with either part time or full time status. While most of the students keep an overall constant enrollment status during their education period, some of them may frequently change their status between full time and part time from one semester to the next. The goal of this research is to exploit the historic patterns to estimate and categorize students$'$ strategy in three different groups of part time, full time and mixed, investigate the educational features of each group and compare their performance. Enrollment strategy refers to the student$'$s mindset for enrollment plan and in one way can be captured from the student$'$s historic enrollment status. Data is collected from the University of Central Florida from 2008 to 2017 and Hidden Markov Model is applied to identify different types of student strategy. Results show that students with Mixed Enrollment Strategy (MES) have features (ex. time to graduation and graduation and halt enrollment ratio) and performances (ex. cumulative GPA) relatively between students with Full time Enrollment Strategy (FES) and students with Part time Enrollment Strategy (PES).

Machine teaching studies the interaction between a teacher and a student/learner where the teacher selects training examples for the learner to learn a specific task. The typical assumption is that the teacher has perfect knowledge of the task---this knowledge comprises knowing the desired learning target, having the exact task representation used by the learner, and knowing the parameters capturing the learning dynamics of the learner. Inspired by real-world applications of machine teaching in education, we consider the setting where teacher's knowledge is limited and noisy, and the key research question we study is the following: When does a teacher succeed or fail in effectively teaching a learner using its imperfect knowledge? We answer this question by showing connections to how imperfect knowledge affects the teacher's solution of the corresponding machine teaching problem when constructing optimal teaching sets. Our results have important implications for designing robust teaching algorithms for real-world applications.

Representation learning approaches require a massive amount of discriminative training data, which is unavailable in many scenarios, such as healthcare, small city, education, etc. In practice, people refer to crowdsourcing to get annotated labels. However, due to issues like data privacy, budget limitation, shortage of domain-specific annotators, the number of crowdsourced labels are still very limited. Moreover, because of annotators' diverse expertises, crowdsourced labels are often inconsistent. Thus, directly applying existing representation learning algorithms may easily get the overfitting problem and yield suboptimal solutions. In this paper, we propose \emph{NeuCrowd}, a unified framework for representation learning from crowdsourced labels. The proposed framework (1) creates a sufficient number of high-quality \emph{n}-tuplet training samples by utilizing safety-aware sampling and robust anchor generation; and (2) automatically learns a neural sampling network that adaptively learns to select effective samples for representation learning network. The proposed framework is evaluated on both synthetic and real-world data sets. The results show that our approach outperforms a wide range of state-of-the-art baselines in terms of prediction accuracy and AUC\footnote{To encourage the reproducible results, we make our code public on a github repository, i.e., \url{https://github.com/crowd-data-mining/NeuCrowd}}.

Link: best udemy course Beginning with Machine Learning & Data Science in Python Fundamentals of Data Science: Exploratory Data Analysis (EDA), Regression (Linear & logistic), Visualization, Basic ML by UNP United Network of Professionals What you'll learn You will be able to apply data science algorithms for solving industry problems You will have a clear understanding of industry standards and best practices for predictive model building You will be able to derive key insights from data using exploratory data analysis techniques You will be able to efficiently handle data in a structured way using Pandas You will have a strong foundation of linear regression, multiple regression and logistic regression You will be able to use python scikit-learn for building different types of regression models You will be able to use cross validation techniques for comparing models, select parameters You will know about common pitfalls in modeling like over-fitting, bias-variance trade off etc.. You will be able to regularize models for reliable predictions Description 85% of data science problems are solved using exploratory data analysis (EDA), visualization, regression (linear & logistic). Naturally, 85% of the interview questions comes from these topics as well. This is a concise course created by UNP to focus on what matter most. This course will help you create a solid foundation of the essential topics of data science.

The Women in AI Podcast - Women at the forefront of AI discuss their work and diversity issues faced in STEM - Listen here. The DeepMind Podcast - A new series that we hope will answer the difficult questions in AI - Listen here. Lex Fridman's AI Podcast - a series of conversations about technology, science, and the human condition hosted by MIT's Lex Fridman - Listen here. The Eye on AI - Justin Gottschlich explains his group's efforts to automate software development - Listen along here. The NVIDIA AI Podcast - NVIDIA release new episodes every other week with guest speakers at the forefront of AI - Listen along here. Artificially Intelligent - Weekly discussions on the impacts of AI - Listen here. Underrated ML - Regular RE•WORK speaker, Sara Hooker & her brother, Sean Hooker have started their new podcast based on underrated ML papers - Listen here. Concerning AI - A series on AI hosted by Ted Sarvata & Brandon Sanders - Listen here.

As with millions of other parents around the world, when our two sons get home from school this afternoon, we have no idea when they'll be going back. Their schools have been hastily scrabbling together remote learning plans, but things are going to be chaotic and unstructured and that's something we'll all have to learn to deal with. What I know for certain is that my boys will have one thing on their mind: video games. What they're picturing (and I can almost see the thought bubbles above their heads when we talk to them about the school closure) is three months in front of the TV playing Apex Legends. You may be in a similar situation in your household, and you may already be feeling guilty about the amount of time your children will end up spending in front of screens simply because you have work to do and their options are limited. The value of video games is normality.

The book AI Crash Course by Hadelin de Ponteves contains a toolkit of four different AI models: Thompson Sampling, Q-Learning, Deep Q-Learning and Deep Convolutional Q-learning. It teaches the theory of these AI models and provides coding examples for solving industry cases based on these models. InfoQ readers can find an excerpt of AI Crash Course on the publisher's website. InfoQ interviewed Hadelin de Ponteves about using different AI models and how to develop AI skills. InfoQ: Why did you write this book?