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Eigenvector Research, Inc. is pleased to announce our 17th annual Eigenvector University. EigenU 2023 includes 16 short courses in chemical data science, i.e. chemometrics. This includes mathematical, statistical, machine learning and artificial intelligence methods as applied to problems in the analysis of data from chemistry and the life sciences. The courses are held in Seattle, USA at the Washington Athletic Club. EigenU also includes a Workshop Dinner, and a PowerUser Tips, Tricks & Poster Session.

Statistical learning models have been growing in popularity in recent years. Many of these models have hyperparameters that must be tuned for models to perform well. Tuning these parameters is not trivial. EZtune is an R package with a simple user interface that can tune support vector machines, adaboost, gradient boosting machines, and elastic net. We first provide a brief summary of the the models that EZtune can tune, including a discussion of each of their hyperparameters. We then compare the ease of using EZtune, caret, and tidymodels. This is followed with a comparison of the accuracy and computation times for models tuned with EZtune and tidymodels. We conclude with a demonstration of how how EZtune can be used to help select a final model with optimal predictive power. Our comparison shows that EZtune can tune support vector machines and gradient boosting machines with EZtune also provides a user interface that is easy to use for a novice to statistical learning models or R.

There is a major lack of diversity in engineering, technology, and computing subjects in higher education. The resulting underrepresentation of some population groups contributes largely to gender and ethnicity pay gaps and social disadvantages. We aim to increase the diversity among students in such subjects by investigating the use of interactive robots as a tool that can get prospective students from different backgrounds interested in robotics as their field of study. For that, we will survey existing solutions that have proven to be successful in engaging underrepresented groups with technical subjects in educational settings. Moreover, we examine two recent outreach events at the University of Hertfordshire against inclusivity criteria. Based on that, we suggest specific activities for higher education institutions that follow an inclusive approach using interactive robots to attract prospective students at open days and other outreach events. Our suggestions provide tangible actions that can be easily implemented by higher education institutions to make technical subjects more appealing to everyone and thereby tackle inequalities in student uptake.

Originally published on Towards AI. Data Analysis Project with Pandas — Step-by-Step Guide (Ted Talks Data) Data Analysis Project Guide — Use Pandas power to get valuable...

Decision-making problems of sequential nature, where decisions made in the past may have an impact on the future, are used to model many practically important applications. In some real-world applications, feedback about a decision is delayed and may arrive via partial rewards that are observed with different delays. Motivated by such scenarios, we propose a novel problem formulation called multi-armed bandits with generalized temporally-partitioned rewards. To formalize how feedback about a decision is partitioned across several time steps, we introduce $\beta$-spread property. We derive a lower bound on the performance of any uniformly efficient algorithm for the considered problem. Moreover, we provide an algorithm called TP-UCB-FR-G and prove an upper bound on its performance measure. In some scenarios, our upper bound improves upon the state of the art. We provide experimental results validating the proposed algorithm and our theoretical results.

Inspired by Regularized Lottery Ticket Hypothesis, which states that competitive smooth (non-binary) subnetworks exist within a dense network, we propose a fewshot class-incremental learning method referred to as Soft-SubNetworks (SoftNet). Our objective is to learn a sequence of sessions incrementally, where each session only includes a few training instances per class while preserving the knowledge of the previously learned ones. SoftNet jointly learns the model weights and adaptive non-binary soft masks at a base training session in which each mask consists of the major and minor subnetwork; the former aims to minimize catastrophic forgetting during training, and the latter aims to avoid overfitting to a few samples in each new training session. We provide comprehensive empirical validations demonstrating that our SoftNet effectively tackles the few-shot incremental learning problem by surpassing the performance of state-of-the-art baselines over benchmark datasets. The public code is available at https://github.com/ihaeyong/ Lifelong Learning, or Continual Learning, is a learning paradigm to expand knowledge and skills through sequential training of multiple tasks (Thrun, 1995). While the standard scenarios for continual learning assume a sufficiently large number of instances per task, a lifelong learner for real-world applications often suffers from insufficient training instances for each problem to solve. This paper aims to tackle the issue of limited training instances for practical Class-Incremental Learning (CIL), referred to as Few-Shot CIL (FSCIL) (Ren et al., 2019; Chen and Lee, 2020; Tao et al., 2020; Zhang et al., 2021; Cheraghian et al., 2021; Shi et al., 2021). However, there are two critical challenges in solving FSCIL problems: catastrophic forgetting and overfitting. Catastrophic forgetting (Goodfellow et al., 2013; Kirkpatrick et al., 2017) or Catastrophic Interference McCloskey and Cohen (1989) is a phenomenon in which a continual learner loses the previously learned task knowledge by updating the weights to adapt to new tasks, resulting in significant performance degeneration on previous tasks.

Looking to enhance your coloring book empire using the power of AI? Then look no further than our'AI Coloring Books Empire' course! Our course offers a complete guide to using AI to produce stunning coloring pages, ideal for publishing in traditional paper books or selling as digital image packs on platforms such as Etsy. Our step-by-step tutorials will teach you how to leverage AI technology to create intricate and visually captivating designs that will appeal to both children and adults. Whether you're a seasoned coloring book creator or just starting out, our course is designed to help take your business to the next level. We'll provide you with all the necessary knowledge to create beautiful and unique designs that will stand out in a crowded market.

Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are sample efficient, and interpretable but often rely on rigid assumptions. Furthermore, direct numerical approximation is usually computationally intensive, requiring significant computational resources and expertise, and many real-world systems do not have fully-known governing laws. While deep learning (DL) provides novel alternatives for efficiently recognizing complex patterns and emulating nonlinear dynamics, its predictions do not necessarily obey the governing laws of physical systems, nor do they generalize well across different systems. Thus, the study of physics-guided DL emerged and has gained great progress. Physics-guided DL aims to take the best from both physics-based modeling and state-of-the-art DL models to better solve scientific problems. In this paper, we provide a structured overview of existing methodologies of integrating prior physical knowledge or physics-based modeling into DL, with a special emphasis on learning dynamical systems. We also discuss the fundamental challenges and emerging opportunities in the area.

Time series is the most prevalent form of input data for educational prediction tasks. The vast majority of research using time series data focuses on hand-crafted features, designed by experts for predictive performance and interpretability. However, extracting these features is labor-intensive for humans and computers. In this paper, we propose an approach that utilizes irregular multivariate time series modeling with graph neural networks to achieve comparable or better accuracy with raw time series clickstreams in comparison to hand-crafted features. Furthermore, we extend concept activation vectors for interpretability in raw time series models. We analyze these advances in the education domain, addressing the task of early student performance prediction for downstream targeted interventions and instructional support. Our experimental analysis on 23 MOOCs with millions of combined interactions over six behavioral dimensions show that models designed with our approach can (i) beat state-of-the-art educational time series baselines with no feature extraction and (ii) provide interpretable insights for personalized interventions. Source code: https://github.com/epfl-ml4ed/ripple/.