Student performance modelling (SPM) is a critical step to assessing and improving students performances in their learning discourse. However, most existing SPM are based on statistical approaches, which on one hand are based on probability, depicting that results are based on estimation; and on the other hand, actual influences of hidden factors that are peculiar to students, lecturers, learning environment and the family, together with their overall effect on student performance have not been exhaustively investigated. In this paper, Student Performance Models (SPM) for improving students performance in programming courses were developed using M5P Decision Tree (MDT) and Linear Regression Classifier (LRC). The data used was gathered using a structured questionnaire from 295 students in 200 and 300 levels of study who offered Web programming, C or JAVA at Federal University, Oye-Ekiti, Nigeria between 2012 and 2016. Hidden factors that are significant to students performance in programming were identified. The relevant data gathered, normalized, coded and prepared as variable and factor datasets, and fed into the MDT algorithm and LRC to develop the predictive models. The evaluation results obtained indicate that the variable-based LRC produced the best model in terms of MAE, RMSE, RAE and the RRSE having yielded the least values in all the evaluations conducted. Further results obtained established the strong significance of attitude of students and lecturers, fearful perception of students, erratic power supply, university facilities, student health and students attendance to the performance of students in programming courses. The variable-based LRC model presented in this paper could provide baseline information about students performance thereby offering better decision making towards improving teaching/learning outcomes in programming courses.

Cloud OnBoard is a free, instructor-led training event, that will provide you with a technical introduction to the Google Cloud Platform (GCP). Through a combination of presentations and technical demonstrations, you will learn how to get started with virtual machines, containers, applications, big data, and machine learning.

GREENLIGHT, Business coaching firm, launches a new Artificial Intelligence (AI) simulator product to train startup founders how to overcome obstacles to be sustainable and profitable. The product was code-named "Crucible". A team of Artificial Intelligence (AI) tech developers, gaming experts, and serial entrepreneurs brought their domain expertise into a continuous learning platform and designed crucible product. A proprietary Smart Start framework from Greenlight is ued for assessing and scoring managerial competency and further improving capability with targeted action plans and simulating successful outcomes. Crucible was tested with startups from Columbia University and several candidates competing in the IBM Watson AI XPRIZE.

If you have little experience in applying machine learning algorithm, you would have discovered that it does not require any knowledge of Statistics as a prerequisite. However, knowing some statistics can be beneficial to understand machine learning technically as well intuitively. Knowing some statistics will eventually be required when you want to start validating your results and interpreting them. After all, when there is data, there are statistics. Like Mathematics is the language of Science. Statistics is one of a kind language for Data Science and Machine Learning. Statistics is a field of mathematics with lots of theories and findings. However, there are various concepts, tools, techniques, and notations are taken from this field to make machine learning what it is today. You can use descriptive statistical methods to help transform observations into useful information that you will be able to understand and share with others.

Learn to design and create video games using the Java programming language and the LibGDX software library. Working through the examples in this book, you will create 12 game prototypes in a variety of popular genres, from collection-based and shoot-em-up arcade games to side-scrolling platformers and sword-fighting adventure games. With the flexibility provided by LibGDX, specialized genres such as card games, rhythm games, and visual novels are also covered in this book. Major updates in this edition include chapters covering advanced topics such as alternative sources of user input, procedural content generation, and advanced graphics. Appendices containing examples for game design documentation and a complete JavaDoc style listing of the extension classes developed in the book have also been added.

One of my professional goals this year was to learn more about artificial intelligence (AI). Over the course of the past year, there have been a lot of stories coming out about how schools are adding the concept of artificial intelligence into their curriculum or trying to weave it into different courses offered. The purpose is to help students better understand its capabilities and how it might impact the future of learning and the future of work. When I did some research earlier this year, I was amazed at some of the different uses of artificial intelligence that we interact with each day, and may not realize. A quick Google search of the term "artificial intelligence" turns up 518 million results in .17

Model ensembles can achieve lower generalization error than single models but are challenging to develop with deep learning neural networks given the computational cost of training each single model. An alternative is to train multiple model snapshots during a single training run and combine their predictions to make an ensemble prediction. A limitation of this approach is that the saved models will be similar, resulting in similar predictions and predictions errors and not offering much benefit from combining their predictions. Effective ensembles require a diverse set of skillful ensemble members that have differing distributions of prediction errors. One approach to promoting a diversity of models saved during a single training run is to use an aggressive learning rate schedule that forces large changes in the model weights and, in turn, the nature of the model saved at each snapshot. In this tutorial, you will discover how to develop snapshot ensembles of models saved using an aggressive learning rate schedule over a single training run. How to Develop a Snapshot Ensemble Deep Learning Neural Network in Python With Keras Photo by Jason Jacobs, some rights reserved.

It's been a year since Spectre and Meltdown -- the hardware vulnerabilities discovered collaboratively by Google's Project Zero and others -- went public. Those vulnerabilities rightly garnered great attention as they and later exploits built in their image affected almost every contemporary CPU on earth.

Machine learning is increasing in popularity and is a buzzword in the quantitative finance community. After all, it is a branch of artificial intelligence where algorithms and mathematical models are used to progressively improve performance on a specific task. Today we will be covering the basic framework of coding out a machine learning algorithm on FXCM's CFD index, SPX500. This article is based on the free course Introduction to Machine Learning by QuantInsti. The machine learning algorithm in this article will learn from basic open and close data.

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