Welcome to this e-learning course developed and produced by Dr Neil Thompson and hosted by Simpliv. Neil is a well-published author in the people professions field, an international conference speaker and sought-after consultant.The overall aim of this course is to help you broaden and deepen your understanding of what is involved in learning, what can prevent it from happening and what you can do to maximize your learning. Learning is part of everyday life and something we are very familiar with. But, that does not mean that we are making the most of the learning opportunities we encounter. Indeed, it is fair to say that, despite the emphasis on the importance of learning, relatively few people achieve optimal learning.

The Dogs versus Cats Redux: Kernels Edition playground competition revived one of our favorite "for fun" image classification challenges from 2013, Dogs versus Cats. This time Kaggle brought Kernels, the best way to share and learn from code, to the table while competitors tackled the problem with a refreshed arsenal including TensorFlow and a few years of deep learning advancements. In this winner's interview, Kaggler Bojan Tunguz shares his 4th place approach based on deep convolutional neural networks and model blending. I am a Theoretical Physicist by training, and have worked in Academia for many years. A few years ago I came across some really cool online machine learning courses, and fell in love with that field.

Arthur Samuel first coined machine learning in the year 1959. It is the field of computer science that uses statistical techniques. It gives computer systems the ability to learn with data without being explicitly programmed. Data science, on the other hand, is an interdisciplinary field of scientific methods, processes, algorithms, and systems that extract knowledge from data in various forms, either structured or unstructured, similar to data mining. The development of these two made research a lot easier.

Being able to perform machine learning in C will make you a very desirable hiring target. Not that you wouldn't be if you used any other language but, the truth is that machine learning in C is a great combination that is likely to give you access to very interesting positions! In this course, we focus on the practical part of machine learning--employing different C libraries. Several popular machine learning libraries currently exist--we'll review them and you'll become familiar with four of them. We use examples of standard machine learning algorithms implemented through the libraries.

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages -- from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we reflect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.

One of the most fundamental and well-studied questions in learning theory is whether one can learn a given problem using an optimization oracle. For online learning in games, it was shown by Kalai and Vempala (2005) that an optimization oracle giving the best decision in hindsight is sufficient for attaining optimal regret. However, in many non-convex settings, such an optimization oracle is either unavailable or NPhard to compute. In contrast, in many such cases, efficient approximation algorithms are usually known, and are guaranteed to return a solution within a certain multiplicative factor of the optimum.

In this paper, we present a framework for Question Difficulty and Expertise Estimation (QDEE) in Community Question Answering sites (CQAs) such as Yahoo! Answers and Stack Overflow, which tackles a fundamental challenge in crowdsourcing: how to appropriately route and assign questions to users with the suitable expertise. This problem domain has been the subject of much research and includes both language-agnostic as well as language conscious solutions. We bring to bear a key language-agnostic insight: that users gain expertise and therefore tend to ask as well as answer more difficult questions over time. We use this insight within the popular competition (directed) graph model to estimate question difficulty and user expertise by identifying key hierarchical structure within said model. An important and novel contribution here is the application of "social agony" to this problem domain. Difficulty levels of newly posted questions (the cold-start problem) are estimated by using our QDEE framework and additional textual features. We also propose a model to route newly posted questions to appropriate users based on the difficulty level of the question and the expertise of the user. Extensive experiments on real world CQAs such as Yahoo! Answers and Stack Overflow data demonstrate the improved efficacy of our approach over contemporary state-of-the-art models. The QDEE framework also allows us to characterize user expertise in novel ways by identifying interesting patterns and roles played by different users in such CQAs.

High above the Hudson River in downtown Jersey City, a former U.S. Secret Service agent named Peter Cavicchia III ran special ops for JPMorgan Chase & Co. His insider threat group--most large financial institutions have one--used computer algorithms to monitor the bank's employees, ostensibly to protect against perfidious traders and other miscreants. Aided by as many as 120 "forward-deployed engineers" from the data mining company Palantir Technologies Inc., which JPMorgan engaged in 2009, Cavicchia's group vacuumed up emails and browser histories, GPS locations from company-issued smartphones, printer and download activity, and transcripts of digitally recorded phone conversations. Palantir's software aggregated, searched, sorted, and analyzed these records, surfacing keywords and patterns of behavior that Cavicchia's team had flagged for potential abuse of corporate assets. Palantir's algorithm, for example, alerted the insider threat team when an employee started badging into work later than usual, a sign of potential disgruntlement. That would trigger further scrutiny and possibly physical surveillance after hours by bank security personnel. Over time, however, Cavicchia himself went rogue. Former JPMorgan colleagues describe the environment as Wall Street meets Apocalypse Now, with Cavicchia as Colonel Kurtz, ensconced upriver in his office suite eight floors above the rest of the bank's security team. People in the department were shocked that no one from the bank or Palantir set any real limits.

Amazon has unveiled a new set of skills for its Echo smart speakers called Alexa Blueprints. The new feature make it easy for anyone to create custom responses to Alexa queries. There's no code to write, no files to upload, and really nothing to learn. Anyone with a web browser can create a custom skill in mere minutes that will be accessible to any Echo device in your home. When I tried it out this morning, I didn't even need to watch the minute-long instructional video to figure it out.

Welcome to this course: Using Deep Learning To Make Decisions. Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. Deep learning is synonymous with machine learning, and simply an advanced subset of that larger field. Technically speaking, deep learning is an umbrella term for a set of neural nets that consist of three or more layers; i.e. at least one hidden layer, and the visible layers of input and output. So what is deep learning capable and incapable of?