Robot control for tactile feedback-based manipulation can be difficult due to the modeling of physical contacts, partial observability of the environment, and noise in perception and control. This work focuses on solving partial observability of contact-rich manipulation tasks as a Sequence-to-Sequence (Seq2Seq)} Imitation Learning (IL) problem. The proposed Seq2Seq model produces a robot-environment interaction sequence to estimate the partially observable environment state variables. Then, the observed interaction sequence is transformed to a control sequence for the task itself. The proposed Seq2Seq IL for tactile feedback-based manipulation is experimentally validated on a door-open task in a simulated environment and a snap-on insertion task with a real robot. The model is able to learn both tasks from only 50 expert demonstrations, while state-of-the-art reinforcement learning and imitation learning methods fail.

Machine Learning Essentials (2023) Kickstart Machine Learning, understand maths behind essential algorithms, implement them in python & build 8 projects! This hands-on course is Machine Learning Essentials (2023) designed for absolute beginners as well as for proficient programmers who want kickstart Machine Learning for solving real life problems. You will learn how to work with data, and train models capable of making "intelligent decisions" Data Science has one of the most rewarding jobs of the 21st century and fortune-500 tech companies are spending heavily on data scientists! Data Science as a career is very rewarding and offers one of the highest salaries in the world. Unlike other courses, which cover only library-implementations this course is Machine Learning Essentials (2023) designed to give you a solid foundation in Machine Learning by covering maths and implementation from scratch in Python for most statistical techniques.

ChatGPT is an AI program that can talk to anyone on the internet via text in a conversational mode. Meaning, the tool can create descriptive texts based on questions, keywords, etc. The AI and ML company OpenAI developed this tool using various ML models. One of the major ML model that the developer use is the Reinforcement Learning model. The AI and ML tool utilizes trial and error, rewards, and human feedback to learn. Slowly, it develops a database of learning models.

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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.