In 2019, the United Nations Educational, Scientific, and Cultural Organization (UNESCO) formulated a plan for the integration of artificial intelligence with education, and suggested that AI has the potential to create a more equitable, efficient, and innovative educational system (1). Although this statement was made prior to Covid-19's rapid proliferation, it warrants renewed consideration in light of the calamitous effects the pandemic has had on the education system. The abrupt shift to remote learning and subsequent return to in-person instruction have revealed significant decreases in learning outcomes and readiness to meet academic standards (2); decreases that a return to normalcy has little hope of addressing. Now, if ever, is the time to look towards innovative means of enhancing education, and artificial intelligence could prove to be the change required to bring about meaningful academic growth. Recent studies indicate that in the United States, school closures and remote learning have resulted in significant decreases in educational outcomes for K-5 students that roughly equate to a five month deficit in math skills, and a four month deficit in reading comprehension (2).

When discussing artificial intelligence (AI), it's hard not to wax philosophically about what intelligence is and what it means to be human. That's not what we're here to discuss. We're going to narrow the scope and look at AI as a computer system that can perform functions that typically require human intelligence such as visual perception, speech recognition and translation, and even decision making. More directly, we're going to talk about AI in education. AI in education refers to the use of artificial intelligence – computers that mimic human perception and decision making to complete a task – in the classroom and management of the class and course load.

I'm looking for someone with a background in education and/or cognitive science to run an online seminar for non-rationalists on how humans learn things and how to efficiently teach a subject to others. A few examples of the sort of content I'm thinking of are: Ebbinghaus's research on memory, spaced repetition, the difference between shallow and deep learning of a subject. The exact content would be up to you. It would be a 1 hour seminar on May 29th, run via Zoom or a similar platform. If you're interested, please email me to discuss the details.

Probabilistic modelling, which falls under the Bayesian paradigm, is gaining popularity world-wide. Its powerful capabilities, such as giving a reliable estimation of its own uncertainty, makes Gaussian process regression a must-have skill for any data scientist. Gaussian process regression is especially powerful when applied in the fields of data science, financial analysis, engineering and geostatistics. This course covers the fundamental mathematical concepts needed by the modern data scientist to confidently apply Gaussian process regression. The course also covers the implementation of Gaussian process regression in Python.

After taking this course, students will be able to understand and implement in Python algorithms of Unsupervised Machine Learning and apply them to real-world datasets. Unsupervised Machine Learning involves finding patterns in datasets. Has a detailed presentation of the the math underlying the above algorithms, including normal distributions, expectation maximization, and singular value decomposition. The course codes are then used to address case studies involving real-world data to perform dimension reduction/clustering for the Iris Flowers Dataset, MNIST Digits Dataset (images), and BBC Text Dataset (articles). All resources (presentations, supplementary documents, demos, codes, solutions to exercises) are downloadable from the course Github site.

Nothing needs to be downloaded and everything is absolutely free. This is truly one of the best resources for anyone, even a non-machine learning person, and I am very happy to share it with you. And the last section in this article is the most important. This is the answer to the question "how to start reading ML research articles?". And if I had something to say, I would definitely write a separate article about it.

Technological advancement is helping many sectors and industries and this is also helpful in the educational sector too. The role of technological advancement especially in the form of Artificial Intelligence in the educational sector was realized very strongly during the Covid-19 pandemic when the students were forced to engage in remote learning. Even though the pandemic has subsided, remote learning and remote working are still relevant and as a result, hybrid education is something that has become a trend. Here are a few ways through which Artificial Intelligence is revolutionizing the educational sector. Educational tasks often involve reported tasks, this is true for both the administrative tasks of the education sector and also, managing the reports of the students. Manually, doing this repeated task is often a waste of time.

Artificial intelligence offers new opportunities to improve university education. This is demonstrated by the Learning Intelligent System (LIS) project, which has been developed by researchers at the Universitat Oberta de Catalunya (UOC) with backing from the eLearning Innovation Center. The system was created by a transdisciplinary research team at the UOC and has already produced excellent results over the past year. It shows how an automatic system can be used to help students who are at risk of failing or dropping out to improve their academic performance. In 2021, a team from the UOC's Faculty of Computer Science, Multimedia and Telecommunications published a study in the International Journal of Educational Technology in Higher Education (ETHE) on the ability of LIS to successfully identify students at risk of failing a course.

We study the first gradient descent step on the first-layer parameters $\boldsymbol{W}$ in a two-layer neural network: $f(\boldsymbol{x}) = \frac{1}{\sqrt{N}}\boldsymbol{a}^\top\sigma(\boldsymbol{W}^\top\boldsymbol{x})$, where $\boldsymbol{W}\in\mathbb{R}^{d\times N}, \boldsymbol{a}\in\mathbb{R}^{N}$ are randomly initialized, and the training objective is the empirical MSE loss: $\frac{1}{n}\sum_{i=1}^n (f(\boldsymbol{x}_i)-y_i)^2$. In the proportional asymptotic limit where $n,d,N\to\infty$ at the same rate, and an idealized student-teacher setting, we show that the first gradient update contains a rank-1 "spike", which results in an alignment between the first-layer weights and the linear component of the teacher model $f^*$. To characterize the impact of this alignment, we compute the prediction risk of ridge regression on the conjugate kernel after one gradient step on $\boldsymbol{W}$ with learning rate $\eta$, when $f^*$ is a single-index model. We consider two scalings of the first step learning rate $\eta$. For small $\eta$, we establish a Gaussian equivalence property for the trained feature map, and prove that the learned kernel improves upon the initial random features model, but cannot defeat the best linear model on the input. Whereas for sufficiently large $\eta$, we prove that for certain $f^*$, the same ridge estimator on trained features can go beyond this "linear regime" and outperform a wide range of random features and rotationally invariant kernels. Our results demonstrate that even one gradient step can lead to a considerable advantage over random features, and highlight the role of learning rate scaling in the initial phase of training.

In reinforcement learning (RL), it is challenging to learn directly from high-dimensional observations, where data augmentation has recently been shown to remedy this via encoding invariances from raw pixels. Nevertheless, we empirically find that not all samples are equally important and hence simply injecting more augmented inputs may instead cause instability in Q-learning. In this paper, we approach this problem systematically by developing a model-agnostic Contrastive-Curiosity-Driven Learning Framework (CCLF), which can fully exploit sample importance and improve learning efficiency in a self-supervised manner. Facilitated by the proposed contrastive curiosity, CCLF is capable of prioritizing the experience replay, selecting the most informative augmented inputs, and more importantly regularizing the Q-function as well as the encoder to concentrate more on under-learned data. Moreover, it encourages the agent to explore with a curiosity-based reward. As a result, the agent can focus on more informative samples and learn representation invariances more efficiently, with significantly reduced augmented inputs. We apply CCLF to several base RL algorithms and evaluate on the DeepMind Control Suite, Atari, and MiniGrid benchmarks, where our approach demonstrates superior sample efficiency and learning performances compared with other state-of-the-art methods.