The design of recommendations strategies in the adaptive learning system focuses on utilizing currently available information to provide individual-specific learning instructions for learners. As a critical motivate for human behaviors, curiosity is essentially the drive to explore knowledge and seek information. In a psychologically inspired view, we aim to incorporate the element of curiosity for guiding learners to study spontaneously. In this paper, a curiosity-driven recommendation policy is proposed under the reinforcement learning framework, allowing for a both efficient and enjoyable personalized learning mode. Given intrinsic rewards from a well-designed predictive model, we apply the actor-critic method to approximate the policy directly through neural networks. Numeric analyses with a large continuous knowledge state space and concrete learning scenarios are used to further demonstrate the power of the proposed method.

Emotions recognition is the task of recognizing people's emotions. Usually it is achieved by analyzing expression of peoples faces. There are two ways for representing emotions: The categorical approach and the dimensional approach by using valence and arousal values. Valence shows how negative or positive an emotion is and arousal shows how much it is activated. Recent deep learning models, that have to do with emotions recognition, are using the second approach, valence and arousal. Moreover, a more interesting concept, which is useful in real life is the "in the wild" emotions recognition. "In the wild" means that the images analyzed for the recognition task, come from from real life sources(online videos, online photos, etc.) and not from staged experiments. So, they introduce unpredictable situations in the images, that have to be modeled. The purpose of this project is to study the previous work that was done for the "in the wild" emotions recognition concept, design a new dataset which has as a standard the "Aff-wild" database, implement new deep learning models and evaluate the results. First, already existing databases and deep learning models are presented. Then, inspired by them a new database is created which includes 507.208 frames in total from 106 videos, which were gathered from online sources. Then, the data are tested in a CNN model based on CNN-M architecture, in order to be sure about their usability. Next, the main model of this project is implemented. That is a Regression GAN which can execute unsupervised and supervised learning at the same time. More specifically, it keeps the main functionality of GANs, which is to produce fake images that look as good as the real ones, while it can also predict valence and arousal values for both real and fake images. Finally, the database created earlier is applied to this model and the results are presented and evaluated.

In the context of HCI, building an automatic system to recognize affect of human facial expression in real-world condition is very crucial to make machine interact naturallisticaly with a man. However, existing facial emotion databases usually contain expression in the limited scenario under well-controlled condition. Aff-Wild is currently the largest database consisting of spontaneous facial expression in the wild annotated with valence and arousal. The first contribution of this project is the completion of extending Aff-Wild database which is fulfilled by collecting videos from YouTube on which the videos have spontaneous facial expressions in the wild, annotating videos with valence and arousal ranging in [-1,1], detecting faces in frames using FFLD2 detector and partitioning the whole data set into train, validate and test set, with 527056, 94223 and 135145 frames. The diversity is guaranteed regarding age, ethnicity and values of valence and arousal. The ratio of male to female is close to 1. Regarding the techniques used to build the automatic system, deep learning is outstanding since almost all winning methods in emotion challenges adopt DNN techniques. The second contribution of this project is that an end-to-end DNN is constructed to have joint CNN and RNN block and gives the estimation on valence and arousal for each frame in sequential data. VGGFace, ResNet, DenseNet with the corresponding pre-trained model for CNN block and LSTM, GRU, IndRNN, Attention mechanism for RNN block are experimented aiming to find the best combination. Fine tuning and transfer learning techniques are also tried out. By comparing the CCC evaluation value on test data, the best model is found to be pre-trained VGGFace connected with 2 layers GRU with attention mechanism. The models test performance is 0.555 CCC for valence with sequence length 80 and 0.499 CCC for arousal with sequence length 70.

Here, we use the nomenclature derived from the meta-learning literature which is the most prevalent at time of writing. Let S {( x l,y l)} L l 1 be a support-set containing input-output pairs, with L equal to one (1-shot) or five (5-shot), and Q { (x m,y m)} M m 1be a query-set (sometimes referred to in the literature as a target-set), with M typically one order of magnitude greater than L. For ease of notation, the support and query sets are grouped in a task T {S, Q}, with the dataset D {T n} N n 1 defined as a collection of such tasks. Models are trained on random tasks sampled from D . Then, given a new task T {S, Q } sampled from a test set, the objective is to condition the model on the samples of the support S to estimate the membership of the samples in the query set Q . In the most common scenario, the inputs x D belong to the same distribution p(x) and are distributed across training, validation, and test sets such that their class membership is non-overlapping. Note that y can be a continuous value (regression) or a discrete one (classification), even though most of the previous work has focused on classification. We also consider the cross-domain scenario, where the inputs are sampled from different distributions at training and test time; this is more representative of real-world scenarios.

The typical multi-task learning methods for spatio-temporal data prediction involve low-rank tensor computation. However, such a method have relatively weak performance when the task number is small, and we cannot integrate it into non-linear models. In this paper, we propose a two-step suboptimal unitary method (SUM) to combine a meta-learning strategy into multi-task models. In the first step, it searches for a global pattern by optimising the general parameters with gradient descents under constraints, which is a geological regularizer to enable model learning with less training data. In the second step, we derive an optimised model on each specific task from the global pattern with only a few local training data. Compared with traditional multi-task learning methods, SUM shows advantages of generalisation ability on distant tasks. It can be applied on any multi-task models with the gradient descent as its optimiser regardless if the prediction function is linear or not. Moreover, we can harness the model to enable traditional prediction model to make coKriging. The experiments on public datasets have suggested that our framework, when combined with current multi-task models, has a conspicuously better prediction result when the task number is small compared to low-rank tensor learning, and our model has a quite satisfying outcome when adjusting the current prediction models for coKriging.

Inspired by trial-to-trial variability in the brain that can result from multiple noise sources, we introduce variability through noise at different levels in a knowledge distillation framework. We introduce "Fickle Teacher" which provides variable supervision signals to the student for the same input. We observe that the response variability from the teacher results in a significant generalization improvement in the student. We further propose "Soft-Randomization" as a novel technique for improving robustness to input variability in the student. This minimizes the dissimilarity between the student's distribution on noisy data with teacher's distribution on clean data. We show that soft-randomization, even with low noise intensity, improves the robustness significantly with minimal drop in generalization. Lastly, we propose a new technique, "Messy-collaboration", which introduces target variability, whereby student and/or teacher are trained with randomly corrupted labels. We find that supervision from a corrupted teacher improves the adversarial robustness of student significantly while preserving its generalization and natural robustness. Our extensive empirical results verify the effectiveness of adding constructive noise in the knowledge distillation framework for improving the generalization and robustness of the model.

Research has found that one of the main hindrances of effective AI deployment within industry, is the inability to demonstrate clear, effective data-strategies with specific results in mind. This course will allow you the unique opportunity to apply effective design thinking methodologies to applied AI and machine learning across a range of industries, to affect positive change within your organization and re-think the way data is being used. This highly interactive and hands-on workshop offers a deep dive into AI, machine learning, and other emerging technologies targeted at leaders responsible for creating disruptive new digital products & services. The program is intended for those with or without a strong background in machine learning, AI, and related technologies - no technical expertise is assumed. Participants can expect to walk away with a comprehensive understanding of how AI and machine learning work as core technologies and a wide range of applications, including; recommendation engines, personalization, predictive analytics, conversational/voice interfaces, and process automation.

Machine learning (ML) is one of the fastest growing areas in technology and a highly sought after skillset in today's job market. The World Economic Forum states the growth of artificial intelligence (AI) could create 58 million net new jobs in the next few years, yet it's estimated that currently there are 300,000 AI engineers worldwide, but millions are needed. This means there is a unique and immediate opportunity for you to get started with learning the essential ML concepts that are used to build AI applications – no matter what your skill levels are. Learning the foundations of ML now, will help you keep pace with this growth, expand your skills and even help advance your career. This course will teach you how to get started with AWS Machine Learning.

TDWI has partnered with MicroTek to offer virtual classroom opportunities to our students at several of our 2018 Seminars. The Virtual Training Room enables remote attendees to experience the benefits of instructor-led training without having to travel. Remote participants experience the same collaboration, instructor interaction, and learning benefits as those who are physically in the classroom. TDWI's Virtual Training Room technology allows all students to: All remote users need to participate in a Virtual Training Room event is a computer with a camera, wired* internet connection, speakers, and a microphone -- it's that easy. PLEASE NOTE: During registration, you will have the option of selecting in-person or virtual attendance.

"Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties. Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? Please explain why you chose the answer you did and sketch out a vision of how the human-machine/AI collaboration will function in 2030.