The ever growing abundance of learning traces in the online learning platforms promises unique insights into the learner knowledge assessment (LKA), a fundamental personalized-tutoring technique for enabling various further adaptive tutoring services in these platforms. Precise assessment of learner knowledge requires the fine-grained Q-matrix, which is generally designed by experts to map the items to skills in the domain. Due to the subjective tendency, some misspecifications may degrade the performance of LKA. Some efforts have been made to refine the small-scale Q-matrix, however, it is difficult to extend the scalability and apply these methods to the large-scale online learning context with numerous items and massive skills. Moreover, the existing LKA models employ flexible deep learning models that excel at this task, but the adequacy of LKA is still challenged by the representation capability of the models on the quite sparse item-skill graph and the learners' exercise data. To overcome these issues, in this paper we propose a prerequisite-driven Q-matrix refinement framework for learner knowledge assessment (PQRLKA) in online context. We infer the prerequisites from learners' response data and use it to refine the expert-defined Q-matrix, which enables the interpretability and the scalability to apply it to the large-scale online learning context. Based on the refined Q-matrix, we propose a Metapath2Vec enhanced convolutional representation method to obtain the comprehensive representations of the items with rich information, and feed them to the PQRLKA model to finally assess the learners' knowledge. Experiments conducted on three real-world datasets demonstrate the capability of our model to infer the prerequisites for Q-matrix refinement, and also its superiority for the LKA task.

TMIC is an App Inventor extension for the deployment of ML models for image classification developed with Google Teachable Machine in educational settings. Google Teachable Machine, is an intuitive visual tool that provides workflow-oriented support for the development of ML models for image classification. Aiming at the usage of models developed with Google Teachable Machine, the extension TMIC enables the deployment of the trained models exported as TensorFlow.js to Google Cloud as part of App Inventor, one of the most popular block-based programming environments for teaching computing in K-12. The extension was created with the App Inventor extension framework based on the extension PIC and is available under the BSD 3 license. It can be used for teaching ML in K-12, in introductory courses in higher education or by anyone interested in creating intelligent apps with image classification. The extension TMIC is being developed by the initiative Computa\c{c}\~ao na Escola of the Department of Informatics and Statistics at the Federal University of Santa Catarina/Brazil as part of a research effort aiming at introducing AI education in K-12.

In Federated Learning (FL) of click-through rate (CTR) prediction, users' data is not shared for privacy protection. The learning is performed by training locally on client devices and communicating only model changes to the server. There are two main challenges: (i) the client heterogeneity, making FL algorithms that use the weighted averaging to aggregate model updates from the clients have slow progress and unsatisfactory learning results; and (ii) the difficulty of tuning the server learning rate with trial-and-error methodology due to the big computation time and resources needed for each experiment. To address these challenges, we propose a simple online meta-learning method to learn a strategy of aggregating the model updates, which adaptively weighs the importance of the clients based on their attributes and adjust the step sizes of the update. We perform extensive evaluations on public datasets. Our method significantly outperforms the state-of-the-art in both the speed of convergence and the quality of the final learning results.

One of the most fundamental problems in machine learning is to compare examples: Given a pair of objects we want to return a value which indicates degree of (dis)similarity. Similarity is often task specific, and pre-defined distances can perform poorly, leading to work in metric learning. However, being able to learn a similarity-sensitive distance function also presupposes access to a rich, discriminative representation for the objects at hand. In this dissertation we present contributions towards both ends. In the first part of the thesis, assuming good representations for the data, we present a formulation for metric learning that makes a more direct attempt to optimize for the k-NN accuracy as compared to prior work. We also present extensions of this formulation to metric learning for kNN regression, asymmetric similarity learning and discriminative learning of Hamming distance. In the second part, we consider a situation where we are on a limited computational budget i.e. optimizing over a space of possible metrics would be infeasible, but access to a label aware distance metric is still desirable. We present a simple, and computationally inexpensive approach for estimating a well motivated metric that relies only on gradient estimates, discussing theoretical and experimental results. In the final part, we address representational issues, considering group equivariant convolutional neural networks (GCNNs). Equivariance to symmetry transformations is explicitly encoded in GCNNs; a classical CNN being the simplest example. In particular, we present a SO(3)-equivariant neural network architecture for spherical data, that operates entirely in Fourier space, while also providing a formalism for the design of fully Fourier neural networks that are equivariant to the action of any continuous compact group.

On June 25, 1949, the British neurologist Geoffrey Jefferson gave a lecture to the Royal College of Surgeons of England entitled The Mind of Mechanical Man. It may be surprising that machine intelligence was the subject of much debate in Jefferson's time, with some describing the 1904s as the period in which artificial intelligence was born following the development of cybernetics. Jefferson's ideas about the intersection of human and machine were ahead of their time and even impressed the great Alan Turing with their prescience and clarity. "[N]ot until a machine can write a sonnet or a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain -- that is, not only write it but know that it had written it," Jefferson said in his lecture. "No mechanism could feel (and not merely artificially signal, an easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or miserable when it cannot get what it wants." Whether they know it or not, critics of artificial intelligence's application in the art world -- and by extension, the world of NFTs -- employ a version of Jefferson's argument when they decry that the technology takes something away from the creative "soul" of artists and their work.

In this article, I am going to map out the things you need to do to become a Data Scientist. This article may solely be for beginners, however, there may be a thing or two that current Junior Data Scientists may have missed out on. This is where I am here to help fill in those gaps so that you don't have to feel imposter syndrome or lack of confidence on your data science journey. I will be taking you through the steps - it is a roadmap at the end of the day. Python is one of the most popular programming languages today and more and more people are adopting it due to its simplicity.

Starting with our very first store on Ocean Avenue in San Francisco, opened almost 50 years ago by Doris and Don Fisher. The thread that's run through those five decades is the phenomenal people that make up our brand – our employees and our customers. People who are rooted in the Legacy that makes Gap what it is, but who are also focused on the future. People who want to leave the world better than they found it. We've built our brand on staying true to our roots while always being out in front of what's next. If you want to be part of an iconic American brand, and help lead the way for where we're headed, we'd love to have you join us About the Role* In this role, you will support the store leadership team by performing functional tasks as assigned. You will act as a mentor and role model to employees to support service behaviors and the execution of tasks in specific areas of expertise. You will focus on leading processes and/or areas of the business, executing tasks and maintaining productivity to ensure goals are met. Through collaboration with your leadership team, your goal is to teach and coach your team and drive behaviors to deliver a best-in-class customer experience What You'll Do* Serve as a role model to achieve priorities in store, with the customer as the primary focus Support the store leadership team to collaborate effectively with employees and ensure work tasks are completed in a timely and efficient manner Build and share expertise in an assigned specialized functional area Support completion or work processes before or after the store closes as needed inclusive of opening and/or closing the store Listen and ask questions to solicit feedback to understand needs and provide service Handle unique or complex customer interactions.Who You Are* Provides clear and direct communication of expectations and gives feedback Ability to utilize technology effectively and engage with customers and your team to meet goals Able to effectively lead and inspire others through coaching and mentoring Demonstrate interest and initiative towards continuous improvement and growth Research process or transaction flow to identify root cause of errors. One of the most competitive Paid Time Off plans in the industry.* Employees can take up to five "on the clock" hours each month to volunteer at a charity of their choice.

Computational Learning Theory: Second European Conference, EuroCOLT '95, Barcelona, Spain, March 13 - 15, 1995. Proceedings (Lecture Notes in Computer Science, 904) [Vitanyi, Paul] on Amazon.com. *FREE* shipping on qualifying offers. Computational Learning Theory: Second European Conference, EuroCOLT '95, Barcelona, Spain, March 13 - 15, 1995. Proceedings (Lecture Notes in Computer Science, 904)

In this course, you will learn how to apply Artificial Intelligence and Machine Learning methods to design new products or systems and solve …

When an agent encounters a continual stream of new tasks in the lifelong learning setting, it leverages the knowledge it gained from the earlier tasks to help learn the new tasks better. In such a scenario, identifying an efficient knowledge representation becomes a challenging problem. Most research works propose to either store a subset of examples from the past tasks in a replay buffer, dedicate a separate set of parameters to each task or penalize excessive updates over parameters by introducing a regularization term. While existing methods employ the general task-agnostic stochastic gradient descent update rule, we propose a task-aware optimizer that adapts the learning rate based on the relatedness among tasks. We utilize the directions taken by the parameters during the updates by additively accumulating the gradients specific to each task. These task-based accumulated gradients act as a knowledge base that is maintained and updated throughout the stream. We empirically show that our proposed adaptive learning rate not only accounts for catastrophic forgetting but also exhibits knowledge transfer. We also show that our method performs better than several state-of-the-art methods in lifelong learning on complex datasets. Moreover, our method can also be combined with the existing methods and achieve substantial improvement in performance. Lifelong learning (LLL), also known as continual learning, is a setting where an agent continuously learns from data belonging to different tasks (Parisi et al., 2019). Here, the goal is to maximize performance on all the tasks arriving in a stream without replaying the entire datasets from past tasks (Riemer et al., 2018). Approaches proposed in this setting involve investigating the stability-plasticity dilemma (Mermillod et al., 2013) in different ways where stability refers to preventing the forgetting of past knowledge and plasticity refers to accumulating new knowledge by learning new tasks (Mermillod et al., 2013; Delange et al., 2021). 1 Unlike human beings, who can efficiently assess the correctness and applicability of the past knowledge (Chen & Liu, 2018), neural networks and other machine learning models often face various issues in this setting. Whenever data from a new task arrives, these models often tend to forget the previously obtained knowledge due to dependency on the input data distribution, limited capacity, diversity among tasks, etc.