In a 2015 interview, Bill Gates imagined a world where Artificially Intelligent Tutoring Systems (AITS) have transformed learning. He spoke of AI-powered tutors offering a personalized approach for each student. They could work with a kid struggling to wrap his head around algebra while his classmates moved on to something more advanced; they could work with a grandmother determined to learn a new language. These systems wouldn't replace teachers. Rather, they'd enhance human teachers' abilities to tailor lessons to each student without knocking their class schedule off track.

Data imbalance remains one of the most widespread problems affecting contemporary machine learning. The negative effect data imbalance can have on the traditional learning algorithms is most severe in combination with other dataset difficulty factors, such as small disjuncts, presence of outliers and insufficient number of training observations. Said difficulty factors can also limit the applicability of some of the methods of dealing with data imbalance, in particular the neighborhood-based oversampling algorithms based on SMOTE. Radial-Based Oversampling (RBO) was previously proposed to mitigate some of the limitations of the neighborhood-based methods. In this paper we examine the possibility of utilizing the concept of mutual class potential, used to guide the oversampling process in RBO, in the undersampling procedure. Conducted computational complexity analysis indicates a significantly reduced time complexity of the proposed Radial-Based Undersampling algorithm, and the results of the performed experimental study indicate its usefulness, especially on difficult datasets.

Non-negative matrix factorization (NMF) minimizes the Euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers. In this paper, we propose a Truncated CauchyNMF loss that handle outliers by truncating large errors, and develop a Truncated CauchyNMF to robustly learn the subspace on noisy datasets contaminated by outliers. We theoretically analyze the robustness of Truncated CauchyNMF comparing with the competing models and theoretically prove that Truncated CauchyNMF has a generalization bound which converges at a rate of order $O(\sqrt{{\ln n}/{n}})$, where $n$ is the sample size. We evaluate Truncated CauchyNMF by image clustering on both simulated and real datasets. The experimental results on the datasets containing gross corruptions validate the effectiveness and robustness of Truncated CauchyNMF for learning robust subspaces.

Recent results in Reinforcement Learning (RL) have shown that agents with limited training environments are susceptible to a large amount of overfitting across many domains. A key challenge for RL generalization is to quantitatively explain the effects of changing parameters on testing performance. Such parameters include architecture, regularization, and RL-dependent variables such as discount factor and action stochasticity. We provide empirical results that show complex and interdependent relationships between hyperparameters and generalization. We further show that several empirical metrics such as gradient cosine similarity and trajectory-dependent metrics serve to provide intuition towards these results.

Inverse reinforcement learning (IRL) enables an agent to learn complex behavior by observing demonstrations from a (near-)optimal policy. The typical assumption is that the learner's goal is to match the teacher's demonstrated behavior. In this paper, we consider the setting where the learner has her own preferences that she additionally takes into consideration. These preferences can for example capture behavioral biases, mismatched worldviews, or physical constraints. We study two teaching approaches: learner-agnostic teaching, where the teacher provides demonstrations from an optimal policy ignoring the learner's preferences, and learner-aware teaching, where the teacher accounts for the learner's preferences. We design learner-aware teaching algorithms and show that significant performance improvements can be achieved over learner-agnostic teaching.

Requirements No Such Pre-req, its good to have some basic math concepts Description Data Science is a multidisciplinary field that deals with the study of data. Data scientists have the ability to take data, understand it, process it, and extract information from it, visualize the information and communicate it. Data scientists are well-versed in multiple disciplines including mathematics, statistics, economics, business, and computer science, as well as the unique ability to ask interesting and challenging data questions based on formal or informal theory to spawn valuable and meticulous insights. This course introduces students to this rapidly growing field and equips them with its most fundamental principles, tools, and mindset. Have an in-depth understanding of the concepts of Machine Learning Be able to grasp, understand, and write machine learning code from scratch Use Builtin Libraries available to build machine learning models Be able to analyze, build, and assess models on any dataset Be able to interpret and understand the black box behind model Understand the applications of data science by exhibiting the ability to work on different datasets and interpreting them.

Earlier this month Google quietly unveiled an incredibly unique opportunity for seasoned developers to explore pilot experiments based on some of Google's frontier AI research, aptly called "AI Workshop." Google already makes a wealth of AI research available on platforms from GitHub to its own AI Hub, complete with a searchable library of ready-to-use code examples, demonstrations and even wrappers around production systems. What makes AI Workshop so different from these other mediums is that it presents an early glimpse at selections from Google's bleeding edge enterprise AI research that might become future product offerings, allowing the research and developer community to provide feedback that can help influence those innovations, granting a rare opportunity to help shape the future of AI in the enterprise. The rise of deep learning has represented a unique era of collaboration between the commercial and research sectors. Many of the underlying toolkits, workflows, algorithms and even research models have all been released under open source licenses, with companies, academics and citizen researchers collaborating together to create innovative new applications and to improve the underlying infrastructure powering the modern deep learning revolution.

More than 80 percent of the world's oceans are currently unmapped, but a $7 million prize pool to explore the deep sea hopes to change that. The Ocean Discovery XPrize was today awarded to teams using uncrewed deep-sea vehicles to map the ocean floor and trace chemical signals underwater. The goal is to develop a comprehensive atlas by 2030. The grand prize required entrants to develop an autonomous vessel capable of mapping at least 250 square kilometres of the sea floor within 24 hours, up to a depth of 4 kilometres below surface level. The maps must be fairly high resolution, with data points taken no more than five metres apart.

This tutorial explains Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) as two fundamental classification methods in statistical and probabilistic learning. We start with the optimization of decision boundary on which the posteriors are equal. Then, LDA and QDA are derived for binary and multiple classes. The estimation of parameters in LDA and QDA are also covered. Then, we explain how LDA and QDA are related to metric learning, kernel principal component analysis, Mahalanobis distance, logistic regression, Bayes optimal classifier, Gaussian naive Bayes, and likelihood ratio test. We also prove that LDA and Fisher discriminant analysis are equivalent. We finally clarify some of the theoretical concepts with simulations we provide.

As application demands for online convex optimization accelerate, the need for designing new methods that simultaneously cover a large class of convex functions and impose the lowest possible regret is highly rising. Known online optimization methods usually perform well only in specific settings, and their performance depends highly on the geometry of the decision space and cost functions. However, in practice, lack of such geometric information leads to confusion in using the appropriate algorithm. To address this issue, some adaptive methods have been proposed that focus on adaptively learning parameters such as step size, Lipschitz constant, and strong convexity coefficient, or on specific parametric families such as quadratic regularizers. In this work, we generalize these methods and propose a framework that competes with the best algorithm in a family of expert algorithms. Our framework includes many of the well-known adaptive methods including MetaGrad, MetaGrad+C, and Ader. We also introduce a second algorithm that computationally outperforms our first algorithm with at most a constant factor increase in regret. Finally, as a representative application of our proposed algorithm, we study the problem of learning the best regularizer from a family of regularizers for Online Mirror Descent. Empirically, we support our theoretical findings in the problem of learning the best regularizer on the simplex and $l_2$-ball in a multiclass learning problem.