The online learning setting has become the most popular regime for studying sequential decision making with dependent and potentially adversarial data. While this paradigm is attractive due to its great generality and minimal set of assumptions [Cesa-Bianchi and Lugosi, 2006], the worstcase nature of the adversary creates statistical and computational challenges [Rakhlin et al., 2015, Littlestone, 1988, Hazan and Koren, 2016]. In order to mitigate these difficulties, Rakhlin et al. [2011] proposed the smoothed setting, wherein the adversary is constrained to sample data from a distribution whose likelihood ratio is bounded above by 1/σ with respect to a fixed dominating measure, which ensures that the adversary cannot choose worst-case inputs with high probability. As in other online learning settings, performance is measured via regret with respect to a best-inhindsight comparator [Cesa-Bianchi and Lugosi, 2006]. Recent works have demonstrated strong computational-statistical tradeoffs in smoothed online learning: while there are statisticaly efficient algorithms that can enjoy regret logarithmic in 1/σ, oracle-efficient algorithms necessarily suffer regret scaling polynomially in 1/σ [Haghtalab et al., 2022a,b, Block et al., 2022], where the learner is assumed access to an Empirical Risk Minimization (ERM) oracle that is able to efficiently optimize functionals on the parameter space. This gap is significant, because in many applications of interest, the natural scaling of σ is exponential in ambient problem dimension [Block and Simchowitz, 2022]. A natural question remains: under which types of smoothing is it possible to design oracleefficient algorithms with regret that scales polynomially in problem dimension? A partial answer was provided by Block and Simchowitz [2022], who demonstrate an efficient algorithm based on the John Ellipsoid which attains log(T/σ) poly(dimension)-regret for noiseless linear classification, and for a suitable generalization to classification with polynomial features.

High-quality education is one of the keys to achieving a more sustainable world. In contrast to traditional face-to-face classroom education, online education enables us to record and research a large amount of learning data for offering intelligent educational services. Knowledge Tracing (KT), which aims to monitor students' evolving knowledge state in learning, is the fundamental task to support these intelligent services. In recent years, an increasing amount of research is focused on this emerging field and considerable progress has been made. In this survey, we categorize existing KT models from a technical perspective and investigate these models in a systematic manner. Subsequently, we review abundant variants of KT models that consider more strict learning assumptions from three phases: before, during, and after learning. To better facilitate researchers and practitioners working on this field, we open source two algorithm libraries: EduData for downloading and preprocessing KT-related datasets, and EduKTM with extensible and unified implementation of existing mainstream KT models. Moreover, the development of KT cannot be separated from its applications, therefore we further present typical KT applications in different scenarios. Finally, we discuss some potential directions for future research in this fast-growing field.

Decentralized Exchanges (DEXs) are new types of marketplaces leveraging Blockchain technology. They allow users to trade assets with Automatic Market Makers (AMM), using funds provided by liquidity providers, removing the need for order books. One such DEX, Uniswap v3, allows liquidity providers to allocate funds more efficiently by specifying an active price interval for their funds. This introduces the problem of finding an optimal strategy for choosing price intervals. We formalize this problem as an online learning problem with non-stochastic rewards. We use regret-minimization methods to show a liquidity provision strategy that guarantees a lower bound on the reward. This is true even for non-stochastic changes to asset pricing, and we express this bound in terms of the trading volume.

The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.

Knowledge tracing aims to model students' past answer sequences to track the change in their knowledge acquisition during exercise activities and to predict their future learning performance. Most existing approaches ignore the fact that students' abilities are constantly changing or vary between individuals, and lack the interpretability of model predictions. To this end, in this paper, we propose a novel model based on ability attributes and attention mechanism. We first segment the interaction sequences and captures students' ability attributes, then dynamically assign students to groups with similar abilities, and quantify the relevance of the exercises to the skill by calculating the attention weights between the exercises and the skill to enhance the interpretability of the model. We conducted extensive experiments and evaluate real online education datasets. The results confirm that the proposed model is better at predicting performance than five well-known representative knowledge tracing models, and the model prediction results are explained through an inference path.

The AWS AI & ML Scholarship Program, in collaboration with Udacity, is an AI/ML-focused scholarship program providing 2,500 scholarships over 2023, as well as mentorship, to students that identify as underserved and underrepresented in technology. The program aims to make the future tech workforce more diverse by removing financial barriers, providing training for careers in tech, and offering mentorship support to individuals who are underserved or underrepresented in tech.

Purpose: The purpose of this study is to predict dropouts in two runs of the same MOOC using an explainable machine learning approach. With the explainable approach, we aim to enable the interpretation of the black-box predictive models from a pedagogical perspective and to produce actionable insights for related educational interventions. The similarity and the differences in feature importance between the predictive models were also examined. Design/Methodology/Approach: This is a quantitative study performed on a large public dataset containing activity logs in a MOOC. In total, 21 features were generated and standardized before the analysis. Multi-layer perceptron neural network was used as the black-box machine learning algorithm to build the predictive models.

Machine learning is an advanced field that incorporates many aspects of mathematics, computer science, and coding. A career in machine learning typically requires a Master's of Science degree. The education and training involved in machine learning can require intense dedication, depth of knowledge, and attention to detail. You can get started with machine learning by learning coding languages, practicing fine-tuning algorithms, and paying close attention to artificial intelligence applications for products and services. Everything from the technology of a Tesla vehicle, Netflix's recommendation algorithms, c or speech-to-text recognition on your iPhone represents an innovation in machine learning. You can find information about machine learning from a breadth of free, accessible resources.

I am a quantum AI research scientist at Zapata Computing in Toronto, Canada, developing machine learning algorithms to work in quantum computers. Before that, I lived in Silicon Valley, where I worked at the following companies: Apple: I was a lead AI educator, in charge of teaching machine learning to the employees and doing internal consulting in AI related projects. Udacity: I was the head of content for AI and Data Science, managing the team that created online courses in AI, ML, Deep Learning, Data Science, etc. Google: I was part of the video recommendations team at YouTube, where we trained machine learning algorithms to recommend videos in the main page. Before my life in technology, I was a research mathematician. I did a Bachelors and Masters at the University of Waterloo, a PhD at the University of Michigan, and an NSERC Postdoctoral Fellowship at the Université du Québec à Montréal.

With the recent advances in data science and artificial intelligence in every industry, including transportation infrastructure and highway operations, it is important for roadway professionals to learn the fundamental components of data science to implement them in their day-to-day practice. Contrary to the general belief, in order to understand and implement these tool and techniques in roadway construction, operations and management, no prior coding or computer programming experience is needed. The main goal of this online training is to introduce the fundamentals of practical data science relevant to transportation and roadway experts. Various aspects, such as the use of different data processing tools, data visualization, data mining and artificial intelligence will be discussed through online hands-on tutorials. Participants will be guided through various interactive course modules and hands-on tutorials to develop skills and knowledge to employ various data science tools on real-world example datasets.