The most progressive, the most cutting-edge, the most exciting… Data science and machine learning are those areas nowadays that are enormously appealing and hot, hot, super-hot topics. But to stay tuned with all the advances and movements in these fields, you need to put lots of effort -- researching, reading, checking all the information, news, guides, and other stuff. This task is far away from being an easy solution. Right now, you can stumble upon a bunch of places with vivid titles and promising headlines, but are they useful enough? Every day I see a crazy flow of information, and, unfortunately, there are lots of false or worthless stuff, and especially on data science and ML.

F.A.C.P. is a fulltime, practicing gastroenterologist and internal medicine physician. As an active holistic health practitioner in the field, Dr. Nandi is also the Chief Health Editor at WXYZ ABC Detroit. At the age of 16, he completed his high school education in Columbus, Ohio where he was awarded a full academic scholarship to The Ohio State University and University of Notre Dame. To remain closer to his family, he chose Ohio State. Partha graduated summa cum laude (Top 1% of the class), a member of Phi Beta Kappa honor society, with a Bachelors degree in chemistry and a minor in classical Greek civilization.

Stuart Russell is a distinguished artificial intelligence researcher, a Professor of Computer Science at the University of California, Berkeley, an Adjunct Professor of Neurological Surgery at the University of California, San Francisco, and leads the Center for Human-Compatible Artificial Intelligence at UC Berkeley. Along with Peter Norvig, Stuart is the author of Artificial Intelligence: A Modern Approach, the most widely used textbook on artificial intelligence. In his most recent book, Human Compatible: AI and the Problem of Control, Stuart proposes a fundamentally new approach to developing AI. In this interview, Stuart warns that AI is reshaping society in unintended ways. For example, social media content selection algorithms that choose what individuals watch and read do not even know that human beings exist. As AI becomes more capable, he suggests that we are going to see bigger failures unless we change the way we think about AI altogether. Stuart argues that to ensure AI is provably beneficial for human beings, we must design machines to be inherently uncertain about human preferences. This way, we can ensure they are humble, altruistic, and committed to pursuing our objectives even as they set their own goals. We also discuss why AI needs regulation similar to civil engineering and medicine, the impact AI is going to make over the next decade, autonomous vehicles, and a variety of other topics.

The graph represents a network of 4,523 Twitter users whose recent tweets contained "#futureofwork", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 10,000 tweets. The network was obtained from Twitter on Tuesday, 25 February 2020 at 08:59 UTC. The tweets in the network were tweeted over the 3-day, 0-hour, 13-minute period from Saturday, 22 February 2020 at 08:19 UTC to Tuesday, 25 February 2020 at 08:32 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.

ML4Chem is an open-source machine learning library for chemistry and materials science. It provides an extendable platform to develop and deploy machine learning models and pipelines and is targeted to the non-expert and expert users. ML4Chem follows user-experience design and offers the needed tools to go from data preparation to inference. Here we introduce its atomistic module for the implementation, deployment, and reproducibility of atom-centered models. This module is composed of six core building blocks: data, featurization, models, model optimization, inference, and visualization. We present their functionality and easiness of use with demonstrations utilizing neural networks and kernel ridge regression algorithms.

This work develops an analytically solvable unsupervised learning scheme that extracts the most informative components for predicting future inputs, termed predictive principal component analysis (PredPCA). Our scheme can effectively remove unpredictable observation noise and globally minimize the test prediction error. Mathematical analyses demonstrate that, with sufficiently high-dimensional observations that are generated by a linear or nonlinear system, PredPCA can identify the optimal hidden state representation, true system parameters, and true hidden state dimensionality, with a global convergence guarantee. We demonstrate the performance of PredPCA by using sequential visual inputs comprising hand-digits, rotating 3D objects, and natural scenes. It reliably and accurately estimates distinct hidden states and predicts future outcomes of previously unseen test input data, even in the presence of considerable observation noise. The simple model structure and low computational cost of PredPCA make it highly desirable as a learning scheme for biological neural networks and neuromorphic chips. Prediction is essential for both biological organisms [1,2] and machine learning [3,4]. In particular, they need to predict the dynamics of newly encountered sensory input data (i.e., test data) based on and only on knowledge learned from a limited number of past experiences (i.e., training data). Generalization error is a standard measure of the generalization capability of predicting the future consequences of previously unseen input data, which is defined as the difference between the training and test prediction errors.

Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice. One approach to study these concerns is through the lens of differential privacy. In this framework, privacy guarantees are generally obtained by perturbing models in such a way that specifics of data used to train the model are made ambiguous. A particular instance of this approach is through a "teacher-student" framework, wherein the teacher, who owns the sensitive data, provides the student with useful, but noisy, information, hopefully allowing the student model to perform well on a given task without access to particular features of the sensitive data. Because stronger privacy guarantees generally involve more significant perturbation on the part of the teacher, deploying existing frameworks fundamentally involves a trade-off between student's performance and privacy guarantee. One of the most important techniques used in previous works involves an ensemble of teacher models, which return information to a student based on a noisy voting procedure. In this work, we propose a novel voting mechanism with smooth sensitivity, which we call Immutable Noisy ArgMax, that, under certain conditions, can bear very large random noising from the teacher without affecting the useful information transferred to the student. Compared with previous work, our approach improves over the state-of-the-art methods on all measures, and scale to larger tasks with both better performance and stronger privacy ($\epsilon \approx 0$). This new proposed framework can be applied with any machine learning models, and provides an appealing solution for tasks that requires training on a large amount of data.

Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data. We survey recent work in inductive logic programming (ILP), a form of machine learning that induces logic programs from data, which has shown promise at addressing these limitations. We focus on new methods for learning recursive programs that generalise from few examples, a shift from using hand-crafted background knowledge to \emph{learning} background knowledge, and the use of different technologies, notably answer set programming and neural networks. As ILP approaches 30, we also discuss directions for future research.

In this video, we will discuss practical considerations in designing a decision tree model. We will discuss how to overcome overfitting in decision trees, three ways to prune the tree, and how to handle missing attributes and continuous values. This channel is part of CSEdu4All, an educational initiative that aims to make computer science education accessible to all! We believe that everyone has the right to good education, and geographical and political boundaries should not be a barrier to obtaining knowledge and information. We hope that you will join and support us in this endeavor!

Gradient Langevin dynamics (GLD) and stochastic GLD (SGLD) have attracted considerable attention lately, as a way to provide convergence guarantees in a non-convex setting. However, the known rates grow exponentially with the dimension of the space. In this work, we provide a convergence analysis of GLD and SGLD when the optimization space is an infinite dimensional Hilbert space. More precisely, we derive non-asymptotic, dimension-free convergence rates for GLD/SGLD when performing regularized non-convex optimization in a reproducing kernel Hilbert space. Amongst others, the convergence analysis relies on the properties of a stochastic differential equation, its discrete time Galerkin approximation and the geometric ergodicity of the associated Markov chains.