Personalized learning is learning experience designed with each student's specific needs in mind. In personalized learning, learning components like pace of learning, content, sequence, technology, content, instructional approach, instructional content and other aspects are adjustable according to the needs and learning purpose of each student. The aim with this more tailored education is to provide relevant learning opportunities that support students as they progress through the learning material. The end aim is to have more students succeed in their studies. Artificial intelligence (AI) is able to capture, aggregate, and analyze data from several different sources to build a student learning profile.

AI in Education Market size valued at USD 4 billion in 2022 and is anticipated to witness over 10% CAGR from 2023 to 2032, owing to the growing inclination towards personalized learning. Increasing reliance on technological reinforcement and conventional techniques has rendered traditional education models no longer sufficient to sustain the sector. In order to fulfill the evolving demands of students and educators, edtech startups are transforming and improving the education sphere by disrupting traditional technologies and advancing existing learning methods. As of January 2023, there are 30 EdTech Unicorns worth $89 billion worldwide. The lack of skilled professionals is a major factor restricting the adoption of AI across the education industry.

With the increase of distance learning, in general, and e-learning, in particular, having a system capable of determining the engagement of students is of primordial importance, and one of the biggest challenges, both for teachers, researchers and policy makers. Here, we present a system to detect the engagement level of the students. It uses only information provided by the typical built-in web-camera present in a laptop computer, and was designed to work in real time. We combine information about the movements of the eyes and head, and facial emotions to produce a concentration index with three classes of engagement: "very engaged", "nominally engaged" and "not engaged at all". The system was tested in a typical e-learning scenario, and the results show that it correctly identifies each period of time where students were "very engaged", "nominally engaged" and "not engaged at all". Additionally, the results also show that the students with best scores also have higher concentration indexes.

Andrew Ng is the Founder of DeepLearning.AI, Founder and CEO of Landing AI, Managing General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer both in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, authoring or co-authoring over 200 research papers in machine learning, robotics, and related fields. He was also the founding lead of the Google Brain team, and Chief Scientist at Baidu, and through this work built the teams that led the AI transformation of two leading internet companies. He is also co-founder and Chairman of Coursera, which had started with his machine learning course. Dr. Ng now focuses his time primarily on his entrepreneurial ventures, looking for the best ways to accelerate responsible AI practices in the larger global economy.

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Welcome to the "Artificial Intelligence with Machine Learning, Deep Learning " course. It's hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon's Alexa and the iPhone's Siri, are all technologies that function based on machine learning algorithms and mathematical models. Machine learning is constantly being applied to new industries and new problems. Whether you're a marketer, video game designer, or programmer, my course on Udemy is here to help you apply machine learning to your work. Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand. Udemy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies. Whether you're interested in machine learning, data mining, or data analysis, Udemy has a course for you. If you want to learn one of the employer's most requested skills?

We study online learning problems in which a decision maker wants to maximize their expected reward without violating a finite set of $m$ resource constraints. By casting the learning process over a suitably defined space of strategy mixtures, we recover strong duality on a Lagrangian relaxation of the underlying optimization problem, even for general settings with non-convex reward and resource-consumption functions. Then, we provide the first best-of-many-worlds type framework for this setting, with no-regret guarantees under stochastic, adversarial, and non-stationary inputs. Our framework yields the same regret guarantees of prior work in the stochastic case. On the other hand, when budgets grow at least linearly in the time horizon, it allows us to provide a constant competitive ratio in the adversarial case, which improves over the best known upper bound bound of $O(\log m \log T)$. Moreover, our framework allows the decision maker to handle non-convex reward and cost functions. We provide two game-theoretic applications of our framework to give further evidence of its flexibility. In doing so, we show that it can be employed to implement budget-pacing mechanisms in repeated first-price auctions.

As a common situation in practice, the forecaster could access some additional resources which we call it side information, We study the effectiveness of stochastic side information in deterministic that may provide some useful knowledge on the online learning scenarios. We propose a forecaster sequence of interest. Cover and Ordentlich [10] first studied to predict a deterministic sequence where its performance is a portfolio investment problem where the sequence of interest evaluated against an expert class. We assume that certain is the stock vectors that may depend on some finite-valued stochastic side information is available to the forecaster but states (as side information), and their proposed forecaster can not the experts. We define the minimax expected regret for achieve the same wealth as the best side information dependent evaluating the forecaster's performance, for which we obtain investment strategy. Xie and Barron [11] studied the case when both upper and lower bounds. Consequently, our results characterize the sequence of interest is generated according to a pair-wise the improvement in the regret due to the stochastic parametric distribution conditioning on the side information, side information. Compared with the classical online learning and derived an logarithmic upper bound of the minimax regret.

In the experts problem, on each of $T$ days, an agent needs to follow the advice of one of $n$ ``experts''. After each day, the loss associated with each expert's advice is revealed. A fundamental result in learning theory says that the agent can achieve vanishing regret, i.e. their cumulative loss is within $o(T)$ of the cumulative loss of the best-in-hindsight expert. Can the agent perform well without sufficient space to remember all the experts? We extend a nascent line of research on this question in two directions: $\bullet$ We give a new algorithm against the oblivious adversary, improving over the memory-regret tradeoff obtained by [PZ23], and nearly matching the lower bound of [SWXZ22]. $\bullet$ We also consider an adaptive adversary who can observe past experts chosen by the agent. In this setting we give both a new algorithm and a novel lower bound, proving that roughly $\sqrt{n}$ memory is both necessary and sufficient for obtaining $o(T)$ regret.

Restless multi-armed bandits (RMABs) extend multi-armed bandits to allow for stateful arms, where the state of each arm evolves restlessly with different transitions depending on whether that arm is pulled. Solving RMABs requires information on transition dynamics, which are often unknown upfront. To plan in RMAB settings with unknown transitions, we propose the first online learning algorithm based on the Whittle index policy, using an upper confidence bound (UCB) approach to learn transition dynamics. Specifically, we estimate confidence bounds of the transition probabilities and formulate a bilinear program to compute optimistic Whittle indices using these estimates. Our algorithm, UCWhittle, achieves sublinear $O(H \sqrt{T \log T})$ frequentist regret to solve RMABs with unknown transitions in $T$ episodes with a constant horizon $H$. Empirically, we demonstrate that UCWhittle leverages the structure of RMABs and the Whittle index policy solution to achieve better performance than existing online learning baselines across three domains, including one constructed from a real-world maternal and childcare dataset.