NTU Library is pleased to present a series of ten workshops on Machine Learning. The objective of this series is to equip NTU staff, students and alumni, with the basic expertise to apply different machine learning concepts and techniques to a wide range of applications using Microsoft Azure Machine Learning Service. Each workshop presents a unique set of content relating to different machine learning concepts and techniques. Each is designed to stand on its own. For example, there is no requirement to attend MLJS03 before attending MLJS04.

This demo would be regarding some of the work that I have already done since starting my journey in Machine Learning. So, there are a lot of MOOCs out there for ML and data science but the most important thing is to apply the concepts learned during the course to solve simple real-world use cases. One of the projects that I did included building state of the art Facial recognition system [VIDEO]. So for that, I referred to several research papers and the foundation was given to me in one of the courses itself, however, it took a lot of effort to connect the dots and that's the fun part. In another project, I made an Emoji Classifier for humans [VIDEO] based on your hand gestures.

A student learning to speak Mandarin wanders into a marketplace on the streets of China on a sunny summer afternoon. Before long, two vendors approach and begin hawking products, trying to outbid one another. The student must now grasp what's being said and formulate an appropriate response using proper pronunciation to avoid being misunderstood. It's a challenging, yet common, scenario for anyone trying to learn a new language by interacting with native speakers and immersing themselves in a foreign culture. Fortunately, the student in this case is able to pause the unfolding scenario to check the accuracy and tone of her planned response.

Emotional Intelligence(also known as Emotional Quotient, or EQ) training courses and workshops in Dubai, the UAE have become very popular recently among managers of all levels. What reason stands behind the popularity of emotional intelligence trainings? A lot of people dream about taking on a managerial or leadership role, but only a few think about the drawbacks and consequences of this role. The responsibility for success or failure always puts a heavy burden on the shoulders of the leader. To manage that pressure and successfully accomplish daily tasks, each leader must have a certain set of characteristics, skills, and traits.

So is it possible to interpret how someone is feeling based on their gait alone? That's exactly what scientists at the University of North Carolina at Chapel Hill and the University of Maryland at College Park have taught a computer to do. Using deep learning, their software can analyze a video of someone walking, turn it into a 3D model, and extract their gait. A neural network then determines the dominant motion and how it matches up to a particular feeling, based on the data on which it's trained. According to their research paper, published in June on arXiv, their deep learning model can guess four different emotions--happy, sad, angry, and neutral--with 80% accuracy.

A self-adaptive system can modify its own structure and behavior at runtime based on its perception of the environment, of itself and of its requirements. To develop a self-adaptive system, software developers codify knowledge about the system and its environment, as well as how adaptation actions impact on the system. However, the codified knowledge may be insufficient due to design time uncertainty, and thus a self-adaptive system may execute adaptation actions that do not have the desired effect. Online learning is an emerging approach to address design time uncertainty by employing machine learning at runtime. Online learning accumulates knowledge at runtime by, for instance, exploring not-yet executed adaptation actions. We address two specific problems with respect to online learning for self-adaptive systems. First, the number of possible adaptation actions can be very large. Existing online learning techniques randomly explore the possible adaptation actions, but this can lead to slow convergence of the learning process. Second, the possible adaptation actions can change as a result of system evolution. Existing online learning techniques are unaware of these changes and thus do not explore new adaptation actions, but explore adaptation actions that are no longer valid. We propose using feature models to give structure to the set of adaptation actions and thereby guide the exploration process during online learning. Experimental results involving four real-world systems suggest that considering the hierarchical structure of feature models may speed up convergence by 7.2% on average. Considering the differences between feature models before and after an evolution step may speed up convergence by 64.6% on average. [...]

However, neural networks' powerful abilities to induce complex representations have come at the cost of data efficiency. Indeed, compared to earlier logical form-based methods, neural networks can sometimes require orders of magnitude more data. The data-hungriness of neural approaches is not surprising - starting with classic logical forms improves data efficiency by presenting a system with pre-made abstractions, where end-to-end neural approaches must do the hard work of inducing abstractions on their own. In this paper, we aim to combine the power of neural networks with the data-efficiency of logical forms by pre-learning abstractions in a semi-supervised way, satiating part of the network's data hunger on cheaper unlabeled data from the environment. When neural nets have only limited data that Figure 1: After seeing this transition, a neural net might generalize this action as stack red blocks to the right of blue blocks except for on brown blocks, but a generalization like stack red blocks on orange blocks is more plausible and generally applicable. We aim to guide our model towards more plausible generalizations by pre-learning inductive biases from observations of the environment.

Decentralized consensus-based optimization is a general computational framework where a network of nodes cooperatively minimizes a sum of locally available cost functions via only local computation and communication. In this article, we survey recent advances on this topic, particularly focusing on decentralized, consensus-based, first-order gradient methods for large-scale stochastic optimization. The class of consensus-based stochastic optimization algorithms is communication-efficient, able to exploit data parallelism, robust in random and adversarial environments, and simple to implement, thus providing scalable solutions to a wide range of large-scale machine learning problems. We review different state-of-the-art decentralized stochastic optimization formulations, different variants of consensus-based procedures, and demonstrate how to obtain decentralized counterparts of centralized stochastic first-order methods. We provide several intuitive illustrations of the main technical ideas as well as applications of the algorithms in the context of decentralized training of machine learning models.

In this paper we present an incremental variant of the Twin Support Vector Machine (TWSVM) called Fuzzy Bounded Twin Support Vector Machine (FBTWSVM) to deal with large datasets and learning from data streams. We combine the TWSVM with a fuzzy membership function, so that each input has a different contribution to each hyperplane in a binary classifier. To solve the pair of quadratic programming problems (QPPs) we use a dual coordinate descent algorithm with a shrinking strategy, and to obtain a robust classification with a fast training we propose the use of a Fourier Gaussian approximation function with our linear FBTWSVM. Inspired by the shrinking technique, the incremental algorithm re-utilizes part of the training method with some heuristics, while the decremental procedure is based on a scored window. The FBTWSVM is also extended for multi-class problems by combining binary classifiers using a Directed Acyclic Graph (DAG) approach. Moreover, we analyzed the theoretical foundations properties of the proposed approach and its extension, and the experimental results on benchmark datasets indicate that the FBTWSVM has a fast training and retraining process while maintaining a robust classification performance.

Management and efficient operations in critical infrastructure such as Smart Grids take huge advantage of accurate power load forecasting which, due to its nonlinear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks, from image classification to machine translation. Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry, but a comprehensive and sound comparison among different architectures is not yet available in the literature. This work aims at filling the gap by reviewing and experimentally evaluating on two real-world datasets the most recent trends in electric load forecasting, by contrasting deep learning architectures on short term forecast (one day ahead prediction). Specifically, we focus on feedforward and recurrent neural networks, sequence to sequence models and temporal convolutional neural networks along with architectural variants, which are known in the signal processing community but are novel to the load forecasting one.