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.

Become a high qualified data scientist by taking these 10 best data science courses on Udemy. Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more! Created by Lazy Programmer Inc. Learn how to apply probability and statistics to real data science and business applications! Created by Lazy Programmer Inc. Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

The recent global pandemic further amplified the impact of online education as an effective alternative that could overcome physical distancing restrictions imposed on students and teaching staff in schools and university campuses. Nevertheless, one of the significant challenges that need to be addressed in online education systems is the ability to effectively trace a student's learning progress, similar to what a human teacher would do in the classroom. Human teachers rely on their intuition and experience to estimate a student's knowledge state and tailor the learning process accordingly. Acquiring such ability would enable online education systems to archive many vital education objectives, including customized curriculum generation, learning materials recommendation, exercise recommendation, automatic evaluation, or learning feedback generation. Achieving such objectives would facilitate automating the teaching process and pave the way for transforming the current online education systems into Intelligent Tutoring Systems (ITS). An ITS not only automates the teaching procedure using computer systems (e.g., web applications) but also handles supporting tasks such as customizing the learning experience and providing guidance and feedback to the students [1]. The Knowledge Tracing (KT) problem formulates the challenge of tracing a student's knowledge state based on their exercise answering history [2, 3]. In particular, the exercise answering history could be represented as a sequence of question-answer pairs, and the task of a solving computational model would be to predict the likelihood of correctly answering the following questions. Figure 1 depicts a probabilistic graphical model for a KT scenario.

To provide rigorous uncertainty quantification for online learning models, we develop a framework for constructing uncertainty sets that provably control risk -- such as coverage of confidence intervals, false negative rate, or F1 score -- in the online setting. This extends conformal prediction to apply to a larger class of online learning problems. Our method guarantees risk control at any user-specified level even when the underlying data distribution shifts drastically, even adversarially, over time in an unknown fashion. The technique we propose is highly flexible as it can be applied with any base online learning algorithm (e.g., a deep neural network trained online), requiring minimal implementation effort and essentially zero additional computational cost. We further extend our approach to control multiple risks simultaneously, so the prediction sets we generate are valid for all given risks. To demonstrate the utility of our method, we conduct experiments on real-world tabular time-series data sets showing that the proposed method rigorously controls various natural risks. Furthermore, we show how to construct valid intervals for an online image-depth estimation problem that previous sequential calibration schemes cannot handle.

Accurate modeling of student knowledge is essential for large-scale online learning systems that are increasingly used for student training. Knowledge tracing aims to model student knowledge state given the student's sequence of learning activities. Modern Knowledge tracing (KT) is usually formulated as a supervised sequence learning problem to predict students' future practice performance according to their past observed practice scores by summarizing student knowledge state as a set of evolving hidden variables. Because of this formulation, many current KT solutions are not fit for modeling student learning from non-assessed learning activities with no explicit feedback or score observation (e.g., watching video lectures that are not graded). Additionally, these models cannot explicitly represent the dynamics of knowledge transfer among different learning activities, particularly between the assessed (e.g., quizzes) and non-assessed (e.g., video lectures) learning activities. In this paper, we propose Transition-Aware Multi-activity Knowledge Tracing (TAMKOT), which models knowledge transfer between learning materials, in addition to student knowledge, when students transition between and within assessed and non-assessed learning materials. TAMKOT is formulated as a deep recurrent multi-activity learning model that explicitly learns knowledge transfer by activating and learning a set of knowledge transfer matrices, one for each transition type between student activities. Accordingly, our model allows for representing each material type in a different yet transferrable latent space while maintaining student knowledge in a shared space. We evaluate our model on three real-world publicly available datasets and demonstrate TAMKOT's capability in predicting student performance and modeling knowledge transfer.

The problem of piecewise affine (PWA) regression and planning is of foundational importance to the study of online learning, control, and robotics, where it provides a theoretically and empirically tractable setting to study systems undergoing sharp changes in the dynamics. Unfortunately, due to the discontinuities that arise when crossing into different ``pieces,'' learning in general sequential settings is impossible and practical algorithms are forced to resort to heuristic approaches. This paper builds on the recently developed smoothed online learning framework and provides the first algorithms for prediction and simulation in PWA systems whose regret is polynomial in all relevant problem parameters under a weak smoothness assumption; moreover, our algorithms are efficient in the number of calls to an optimization oracle. We further apply our results to the problems of one-step prediction and multi-step simulation regret in piecewise affine dynamical systems, where the learner is tasked with simulating trajectories and regret is measured in terms of the Wasserstein distance between simulated and true data. Along the way, we develop several technical tools of more general interest.