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Memory-Based Learning: Instructional Materials


Using machine learning to improve student success in higher education

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Many higher-education institutions are now using data and analytics as an integral part of their processes. Whether the goal is to identify and better support pain points in the student journey, more efficiently allocate resources, or improve student and faculty experience, institutions are seeing the benefits of data-backed solutions. This article is a collaborative effort by Claudio Brasca, Nikhil Kaithwal, Charag Krishnan, Monatrice Lam, Jonathan Law, and Varun Marya, representing views from McKinsey's Public & Social Sector Practice. Those at the forefront of this trend are focusing on harnessing analytics to increase program personalization and flexibility, as well as to improve retention by identifying students at risk of dropping out and reaching out proactively with tailored interventions. Indeed, data science and machine learning may unlock significant value for universities by ensuring resources are targeted toward the highest-impact opportunities to improve access for more students, as well as student engagement and satisfaction.


Solving the Class Imbalance Problem Using a Counterfactual Method for Data Augmentation

arXiv.org Artificial Intelligence

Learning from class imbalanced datasets poses challenges for many machine learning algorithms. Many real-world domains are, by definition, class imbalanced by virtue of having a majority class that naturally has many more instances than its minority class (e.g. genuine bank transactions occur much more often than fraudulent ones). Many methods have been proposed to solve the class imbalance problem, among the most popular being oversampling techniques (such as SMOTE). These methods generate synthetic instances in the minority class, to balance the dataset, performing data augmentations that improve the performance of predictive machine learning (ML) models. In this paper we advance a novel data augmentation method (adapted from eXplainable AI), that generates synthetic, counterfactual instances in the minority class. Unlike other oversampling techniques, this method adaptively combines exist-ing instances from the dataset, using actual feature-values rather than interpolating values between instances. Several experiments using four different classifiers and 25 datasets are reported, which show that this Counterfactual Augmentation method (CFA) generates useful synthetic data points in the minority class. The experiments also show that CFA is competitive with many other oversampling methods many of which are variants of SMOTE. The basis for CFAs performance is discussed, along with the conditions under which it is likely to perform better or worse in future tests.


DisCERN:Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods

arXiv.org Artificial Intelligence

Counterfactual explanations focus on "actionable knowledge" to help end-users understand how a machine learning outcome could be changed to a more desirable outcome. For this purpose a counterfactual explainer needs to discover input dependencies that relate to outcome changes. Identifying the minimum subset of feature changes needed to action an output change in the decision is an interesting challenge for counterfactual explainers. The DisCERN algorithm introduced in this paper is a case-based counter-factual explainer. Here counterfactuals are formed by replacing feature values from a nearest unlike neighbour (NUN) until an actionable change is observed. We show how widely adopted feature relevance-based explainers (i.e. LIME, SHAP), can inform DisCERN to identify the minimum subset of "actionable features". We demonstrate our DisCERN algorithm on five datasets in a comparative study with the widely used optimisation-based counterfactual approach DiCE. Our results demonstrate that DisCERN is an effective strategy to minimise actionable changes necessary to create good counterfactual explanations.


Quran Memorization Course. A Proven System To Do It Easy NOW

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In this Course you will learn and gain 6 new habits. Each habit will make big change in your Memorization Ability. Many people who have taken this course before were able to memorize the whole holy Quran short Time. Even some of them were able to memorize the whole Quran in short Time. This course helped myself and when I noticed the amazing results, I have decided to do this course publicly to help million of Muslims around the world.


quantum Case-Based Reasoning (qCBR)

arXiv.org Artificial Intelligence

Case-Based Reasoning (CBR) is an artificial intelligence approach to problem-solving with a good record of success. This article proposes using Quantum Computing to improve some of the key processes of CBR defining so a Quantum Case-Based Reasoning (qCBR) paradigm. The focus is set on designing and implementing a qCBR based on the variational principle that improves its classical counterpart in terms of average accuracy, scalability and tolerance to overlapping. A comparative study of the proposed qCBR with a classic CBR is performed for the case of the Social Workers' Problem as a sample of a combinatorial optimization problem with overlapping. The algorithm's quantum feasibility is modelled with docplex and tested on IBMQ computers, and experimented on the Qibo framework.


Explainable Goal-Driven Agents and Robots -- A Comprehensive Review

arXiv.org Artificial Intelligence

Recent applications of autonomous agents and robots, such as self-driving cars, scenario-based trainers, exploration robots, and service robots have brought attention to crucial trust-related challenges associated with the current generation of artificial intelligence (AI) systems. AI systems based on the connectionist deep learning neural network approach lack capabilities of explaining their decisions and actions to others, despite their great successes. Without symbolic interpretation capabilities, they are black boxes, which renders their decisions or actions opaque, making it difficult to trust them in safety-critical applications. The recent stance on the explainability of AI systems has witnessed several approaches on eXplainable Artificial Intelligence (XAI); however, most of the studies have focused on data-driven XAI systems applied in computational sciences. Studies addressing the increasingly pervasive goal-driven agents and robots are still missing. This paper reviews approaches on explainable goal-driven intelligent agents and robots, focusing on techniques for explaining and communicating agents perceptual functions (example, senses, and vision) and cognitive reasoning (example, beliefs, desires, intention, plans, and goals) with humans in the loop. The review highlights key strategies that emphasize transparency, understandability, and continual learning for explainability. Finally, the paper presents requirements for explainability and suggests a roadmap for the possible realization of effective goal-driven explainable agents and robots.


Build Facebook Messenger Chatbot with IBM Watson Assistant

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Build Facebook Messenger Chatbot with IBM Watson Assistant - Facebook messenger chatbot Created by Tushar SKumarPreview this course Udemy GET COUPON CODE Chatbots are software agents capable of having interaction with human. The demand for chatbots are increasing everyday and the reason behind this is not implausible. They can also greatly build your brand so it is not surprise that being able to create a chatbot is a very lucrative skill. IBM Watson Assistant is the platform which allows user to utilize Artificial Intelligence without the coding background. After this course you will be able to build chatbot, will can learn by itself by leveraging on Watson's Natural Language Processing (NLP) capabilities.


A Methodological Approach to Model CBR-based Systems

arXiv.org Artificial Intelligence

MLassisted applications are a trend, and many researchers and developers are rushing to apply ML and recover their inherent potential benefits [2] [3]. However, using ML techniques to solve any problem do require some previous background and expertise. For example, it is vital to choose the ML technique that better suits the target application in terms of available computational capability and expected target results. In sequence to an adequate ML technique choice, it is typically necessary to model the problem under the premises of the chosen technique. The modeling process may include, as an example, an MDP-based markovian process (Markov Decision Process) like Q-Learning or SARSA formulation for Reinforcement Learning or the definition of a neural network structure for Neural Networks (NN) [4] [5].


Extreme Memorization via Scale of Initialization

arXiv.org Machine Learning

We construct an experimental setup in which changing the scale of initialization strongly impacts the implicit regularization induced by SGD, interpolating from good generalization performance to completely memorizing the training set while making little progress on the test set. Moreover, we find that the extent and manner in which generalization ability is affected depends on the activation and loss function used, with $\sin$ activation being the most extreme. In the case of the homogeneous ReLU activation, we show that this behavior can be attributed to the loss function. Our empirical investigation reveals that increasing the scale of initialization could cause the representations and gradients to be increasingly misaligned across examples in the same class. We further demonstrate that a similar misalignment phenomenon occurs in other scenarios affecting generalization performance, such as changes to the architecture or data distribution.


C1000-012 IBM Watson Application Developer V3.1

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Udemy Coupon ED C1000-012 IBM Watson Application Developer V3.1 Number of questions: 60 Number of questions to pass: 44 Time allowed: 90 mins Status: Live This exam consists of 5 sections described below.New Created by Mari F Included in This Course 20 questions Practice Tests Test 1 10 questions Test 2 10 questions Description Hard work is one way of achieving goals. There is no famous person or single individual in history who has achieved his or her goals in life without working hard and sweating on them. Whether working more than anyone, studying more than anyone, or even suffering more than everyone else, you need to understand the importance of working towards your ultimate goal, without that, there is no way to have goals in life that are achievable really. To start the hard work, you can set your schedule, write down the tasks and functions of the day and find the right people and resources to help you. Who this course is for: Technology professionals Technology courses instructor since 2019 and database specialist.