In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. This course would enable you to gain insight into the realm of Quantum Computing. The students would be able to learn and develop expertise in Quantum algorithms, gates and implementation of these codes. The undergraduate students would particularly find it very imperative and for realizing their final year projects and reports. Furthermore, this course is an introduction to the fundamental concepts of quantum circuits and algorithms.

Created by Dr. Ryan Ahmed, Ph.D., MBA, Mitchell Bouchard, Ligency Team Created by Dr. Ryan Ahmed, Ph.D., MBA, Mitchell Bouchard, Ligency Team

We introduce a machine-in-the-loop pipeline that aims to address root causes of unwanted bias in natural language based supervised machine learning tasks in the education domain. Learning from the experiences of students is foundational for education researchers, and academic administrators. 21st-century skills learned from experience are becoming a core part of college and career readiness as well as the hiring process in the new knowledge economy. Minoritized students demonstrate these skills in their daily lives, but documenting, assessing, and validating these skills is a huge problem for educational institutions. As an equity focused online platform, LivedX translates minoritized students' lived experiences into the 21st century skills, issues micro-credentials, and creates personal 21st century skills portfolio. To automate the micro credential mining from the natural language texts received from the students' submitted essays, we employed a bag-of-word model to construct a multi-output classifier. Despite our goal, our model initially exacerbated disparate impact on minoritized students. We used a machine-in-the-loop model development pipeline to address the problem and refine the aforementioned model to ensure fairness in its prediction.

Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning-based KT models have shown significant predictive performance compared with traditional models. However, it is difficult to extract psychologically meaningful explanations from the tens of thousands of parameters in neural networks, that would relate to cognitive theory. There are several ways to achieve high accuracy in student performance prediction but diagnostic and prognostic reasoning is more critical in learning sciences. Since KT problem has few observable features (problem ID and student's correctness at each practice), we extract meaningful latent features from students' response data by using machine learning and data mining techniques. In this work, we present Interpretable Knowledge Tracing (IKT), a simple model that relies on three meaningful latent features: individual skill mastery, ability profile (learning transfer across skills), and problem difficulty. IKT's prediction of future student performance is made using a Tree-Augmented Naive Bayes Classifier (TAN), therefore its predictions are easier to explain than deep learning-based student models. IKT also shows better student performance prediction than deep learning-based student models without requiring a huge amount of parameters. We conduct ablation studies on each feature to examine their contribution to student performance prediction. Thus, IKT has great potential for providing adaptive and personalized instructions with causal reasoning in real-world educational systems.

Variational Autoencoders (VAEs) suffer from degenerated performance, when learning several successive tasks. This is caused by catastrophic forgetting. In order to address the knowledge loss, VAEs are using either Generative Replay (GR) mechanisms or Expanding Network Architectures (ENA). In this paper we study the forgetting behaviour of VAEs using a joint GR and ENA methodology, by deriving an upper bound on the negative marginal log-likelihood. This theoretical analysis provides new insights into how VAEs forget the previously learnt knowledge during lifelong learning. The analysis indicates the best performance achieved when considering model mixtures, under the ENA framework, where there are no restrictions on the number of components. However, an ENA-based approach may require an excessive number of parameters. This motivates us to propose a novel Dynamic Expansion Graph Model (DEGM). DEGM expands its architecture, according to the novelty associated with each new databases, when compared to the information already learnt by the network from previous tasks. DEGM training optimizes knowledge structuring, characterizing the joint probabilistic representations corresponding to the past and more recently learned tasks. We demonstrate that DEGM guarantees optimal performance for each task while also minimizing the required number of parameters. Supplementary materials (SM) and source code are available in https://github.com/dtuzi123/Expansion-Graph-Model.

How can an end-user provide feedback if a deployed structured prediction model generates inconsistent output, ignoring the structural complexity of human language? This is an emerging topic with recent progress in synthetic or constrained settings, and the next big leap would require testing and tuning models in real-world settings. We present a new dataset, Interscript, containing user feedback on a deployed model that generates complex everyday tasks. Interscript contains 8,466 data points -- the input is a possibly erroneous script and a user feedback, and the output is a modified script. We posit two use-cases of \ours that might significantly advance the state-of-the-art in interactive learning. The dataset is available at: https://github.com/allenai/interscript.

This AWS SageMaker Canvas Course will help you to become a Machine Learning Expert and will enhance your skills by offering you comprehensive knowledge, and the required hands-on experience on this newly launched Cloud based ML tool, by solving real-time industry-based projects, without needing any complex coding expertise.Top Reasons why you should learn AWS SageMaker Canvas : AWS is the #1 cloud based tool used industry wide for Machine Learning Projects.You do not need Advanced Coding expertise generally required in the field of Machine Learning.Complex knowledge of Statistics, Algorithms, Mathematics that is difficult to master is also not required.Machine Learning Models that usually takes many days to build, are available very quickly in just a few minutes.The demand for ML professionals is on the rise. This is one of the most sought-after profession currently in the lines of Data Science.There are multiple opportunities across the Globe for everyone with Machine Learning skills.SageMaker Canvas has a small learning curve and you can pick up even advanced concepts very quickly.This Tool is available as a part of AWS Free Tier.You do not need high configuration computer to learn this tool. All you need is any system with internet connectivity.Top Reasons why you should choose this Course :This course is designed keeping in mind the students from all backgrounds - hence we cover everything from basics, and gradually progress towards advanced topics.We take live Industry Projects and do each and every step from start to end in the course itself.This course can be completed in a Day !All Doubts will be answered.Most Importantly, Guidance is offered beyond the Tool - You will not only learn the Software, but important Machine Learning principles. Also, I will share the resources where to get the best possible help from, & also the sources to get public datasets to work on to get mastery in the ML domain.A Verifiable Certificate of Completion is presented to all students who undertake this AWS SageMaker Canvas course.

PyTorch and TensorFlow are far and away the two most popular Deep Learning frameworks today. The debate over whether PyTorch or TensorFlow is superior is a longstanding point of contentious debate, with each camp having its share of fervent supporters. Both PyTorch and TensorFlow have developed so quickly over their relatively short lifetimes that the debate landscape is ever-evolving. Outdated or incomplete information is abundant, and further obfuscates the complex discussion of which framework has the upper hand in a given domain. While TensorFlow has a reputation for being an industry-focused framework and PyTorch has a reputation for being a research-focused framework, we'll see that these notions stem partially from outdated information. The conversation about which framework reigns supreme is much more nuanced going into 2022 - let's explore these differences now. PyTorch and TensorFlow alike have unique development stories and complicated design-decision histories. Previously, this has made comparing the two a complicated technical discussion about their current features and speculated future features. Given that both frameworks have matured exponentially since their inceptions, many of these technical differences are vestigial at this point.

You look forward to applying your knowledge of the relevant areas mentioned above, combined with an interest in didactic innovation to the programme of Computational Social Science. For this programme you will firstly prepare the necessary learning materials for the Digital Expertise (DE) trajectory (based on existing learning modules), coordinate the DE activities with the activities of other learning trajectories, and implement the learning activities from September 2022 onwards. The teaching itself will be supported by several student assistants who carry out the practical sessions under your supervision. Besides you will organize input and lectures from available research staff. We offer you a chance to help shape this innovative, interdisciplinary programme.

Tell me in the comment section, which course you like. Some most popular use cases of machine learning in finance are Process Automation, Security, Algorithmic Trading, Robo-advisory, and Underwriting & Credit Scoring. Python is broadly used in finance, especially in quantitative finance. Python is one of the most famous programming languages for Fintech Companies. The HackerRank 2018 Developer Skills Report said that Python was among the top three most popular languages in financial services. On the report of HackerRank, Python, Java, C, C#, C, and Ruby are the six best programming languages for FinTech and finance.