In recent years, functional linear models have attracted growing attention in statistics and machine learning, with the aim of recovering the slope function or its functional predictor. This paper considers online regularized learning algorithm for functional linear models in reproducing kernel Hilbert spaces. Convergence analysis of excess prediction error and estimation error are provided with polynomially decaying step-size and constant step-size, respectively. Fast convergence rates can be derived via a capacity dependent analysis. By introducing an explicit regularization term, we uplift the saturation boundary of unregularized online learning algorithms when the step-size decays polynomially, and establish fast convergence rates of estimation error without capacity assumption. However, it remains an open problem to obtain capacity independent convergence rates for the estimation error of the unregularized online learning algorithm with decaying step-size. It also shows that convergence rates of both prediction error and estimation error with constant step-size are competitive with those in the literature.

Online learning platforms provide learning materials and answers to students' academic questions by experts, peers, or systems. This paper explores question-type identification as a step in content understanding for an online learning platform. The aim of the question-type identifier is to categorize question types based on their structure and complexity, using the question text, subject, and structural features. We have defined twelve question-type classes, including Multiple-Choice Question (MCQ), essay, and others. We have compiled an internal dataset of students' questions and used a combination of weak-supervision techniques and manual annotation. We then trained a BERT-based ensemble model on this dataset and evaluated this model on a separate human-labeled test set. Our experiments yielded an F1-score of 0.94 for MCQ binary classification and promising results for 12-class multilabel classification. We deployed the model in our online learning platform as a crucial enabler for content understanding to enhance the student learning experience.

Decision-focused learning (DFL) was recently proposed for stochastic optimization problems that involve unknown parameters. By integrating predictive modeling with an implicitly differentiable optimization layer, DFL has shown superior performance to the standard two-stage predict-then-optimize pipeline. However, most existing DFL methods are only applicable to convex problems or a subset of nonconvex problems that can be easily relaxed to convex ones. Further, they can be inefficient in training due to the requirement of solving and differentiating through the optimization problem in every training iteration. We propose SO-EBM, a general and efficient DFL method for stochastic optimization using energy-based models. Instead of relying on KKT conditions to induce an implicit optimization layer, SO-EBM explicitly parameterizes the original optimization problem using a differentiable optimization layer based on energy functions. To better approximate the optimization landscape, we propose a coupled training objective that uses a maximum likelihood loss to capture the optimum location and a distribution-based regularizer to capture the overall energy landscape. Finally, we propose an efficient training procedure for SO-EBM with a self-normalized importance sampler based on a Gaussian mixture proposal. We evaluate SO-EBM in three applications: power scheduling, COVID-19 resource allocation, and non-convex adversarial security game, demonstrating the effectiveness and efficiency of SO-EBM.

In this video, you will learn from basics to advanced machine learning concepts from Great Learning's top faculties, including professor Mukesh Rao, Bharani Akella & many other leading industry experts. If you are an enthusiast who wants to start with machine learning from scratch, this machine learning beginner video is the best to start with. Agenda: • Python for Machine Learning • Role of Statistics in Machine Learning • Introduction to Machine Learning and its types • How does a Machine learning model learn? Topics Covered: 00:01:09 – What Is Machine learning?

MATLAB is a widely used programming language for statistical computing. This course is for you if you want to have a real feel of the Machine Learning techniques without having to learn all the complicated maths. Additionally, this course is also for you if you have had previous hours and hours of machine learning theory but could never got a change or figure out how to implement and solve data science problems with it. The approach in this course is very practical and we will start everything from very scratch. We will immediately start coding after a couple of introductory tutorials and we try to keep the theory to bare minimal.

Introduction: If you're new to AI, then this might be the perfect guide for you. In this guide, we will be looking at the different types of AI and how they can be used in your business. We will also look at some tips on how to get started with AI in your business. Artificial intelligence is the process of making a computer system that can think and learn like a human. Artificial intelligence technology is used to create programs that can solve problems and act on behalf of humans.

The aim of this course "Introduction to Automata Theory, Languages and Computation" is to give a detailed working explanation regarding each Mathematical model, its corresponding languages, and their provable equivalence. "Theory of Computation" has three major subdivisions namely

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 4th International Workshop on Reading Music Systems, held online on Nov. 18th 2022.

Computing competency is becoming an essential quality needed by industry. For decades, the gap between baccalaureate computing graduates and industry needs was a discussion topic. Most graduates seek employment in deference to continuing their full-time graduate (master's or doctoral) programs. While the percent of such choice varies by institution, it is estimated that about 5% of computing graduates choose full-time graduate study upon graduation, meaning that 95% of computing graduates seek jobs in business, government, or industry.15 While computing graduates may acquire jobs in today's world, they often lack the competencies (skills and dispositions) expected in the workplace. Most undergraduate computing-degree programs want to produce job-ready graduates who are productive on the first workday. They often seek local advisory boards composed of industry, government, and business representatives to help develop a functional computing curriculum for their students. Information technology and computing disciplines are changing, and new fields appear continuously. Computing curricula and undergraduate programs are challenged to keep up with this rapid change. Employers are looking for competent graduates who can apply the knowledge, skill, and culture they acquire in college to solve problems as soon as they enter the workforce. High-performance computing (HPC) and parallel and distributed computing (PDC) have become pervasive.

We all know the different operators in python, i.e., Unary operators and Binary operators. An operator that can be used to negate a positive value with one operand is called the unary operator; for example, x -4, here we are negating a value 4 with operator –, so operator -- acts as a unary operator. An operator who works with two operands is called a binary operator; for example, x, 3 7, the operator acts as a binary operator.