This is a more traditional course than the others on this list, so if you like structured learning, you will find this course better suited to you. In this course, you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts, and terms like Machine learning, Deep learning, and Neural networks. You will also explore different issues and concerns surrounding AI, such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini-project. By the way, if you find Coursera courses and specialization useful then you should also join the Coursera Plus, a subscription plan from Coursera which provides you unlimited access to their most popular courses, specialization, professional certificate, and guided projects.

IIT Ropar is an engineering, science, and technology higher education institute located in Rupnagar, Punjab, India, imparting state-of-the-art technical education in a variety of fields. Its emphasis on promoting cutting-edge research and high quality publications is the key to its recognition in the international research community. It is a well reputed institute, having secured high ranks in'Times Higher Education (THE) World University Rankings 2020', 'QS India Rankings 2020', and Union HRD Ministry's'National Institutional Ranking Framework (NIRF)', among others.

Meta-reinforcement learning typically requires orders of magnitude more samples than single task reinforcement learning methods. This is because meta-training needs to deal with more diverse distributions and train extra components such as context encoders. To address this, we propose a novel self-supervised learning task, which we named Trajectory Contrastive Learning (TCL), to improve meta-training. TCL adopts contrastive learning and trains a context encoder to predict whether two transition windows are sampled from the same trajectory. TCL leverages the natural hierarchical structure of context-based meta-RL and makes minimal assumptions, allowing it to be generally applicable to context-based meta-RL algorithms. It accelerates the training of context encoders and improves meta-training overall. Experiments show that TCL performs better or comparably than a strong meta-RL baseline in most of the environments on both meta-RL MuJoCo (5 of 6) and Meta-World benchmarks (44 out of 50).

Reinforcement learning (RL) has gained growing popularity in many human-computer interaction (HCI) applications [1, 2, 3]. In digital health interventions, for example, RL is a natural choice for personalization as RL agents can continuous adapt their strategies based on users' responses to the interventions [3]. Moreover, the recent advances in interactive RL calls for contributions from HCI researchers to improve the efficiency of RL algorithms [4]. While there is a natural fit between RL and HCI, the well-known data greedy property of reinforcement learning makes the RL-based systems often suffer from the cold start problem [5]. In HCI, as very few (or even no) experiences with users are available at the beginning in general, RL agents are required to interact many times with users prior to performing well. Many researchers had made efforts to overcome this challenge by shortening the learning process. Several approaches have been proposed to perform a faster online learning so that less interactions are needed in practice. For instance, Tabatabaei et al. [6] and Tomkins et al. [7] make RL algorithms quickly learn from the limited experience This is a preprint of our position paper presented to the "Reinforcement Learning for Humans, Computer, and Interaction (RL4HCI)" workship at ACM CHI2021, https://sites.google.com/view/rl4hci/home. The preprint is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Dysarthria is a condition which hampers the ability of an individual to control the muscles that play a major role in speech delivery. The loss of fine control over muscles that assist the movement of lips, vocal chords, tongue and diaphragm results in abnormal speech delivery. One can assess the severity level of dysarthria by analyzing the intelligibility of speech spoken by an individual. Continuous intelligibility assessment helps speech language pathologists not only study the impact of medication but also allows them to plan personalized therapy. It helps the clinicians immensely if the intelligibility assessment system is reliable, automatic, simple for (a) patients to undergo and (b) clinicians to interpret. Lack of availability of dysarthric data has resulted in development of speaker dependent automatic intelligibility assessment systems which requires patients to speak a large number of utterances. In this paper, we propose (a) a cost minimization procedure to select an optimal (small) number of utterances that need to be spoken by the dysarthric patient, (b) four different speaker independent intelligibility assessment systems which require the patient to speak a small number of words, and (c) the assessment score is close to the perceptual score that the Speech Language Pathologist (SLP) can relate to. The need for small number of utterances to be spoken by the patient and the score being relatable to the SLP benefits both the dysarthric patient and the clinician from usability perspective.

Recent studies on computer vision mainly focus on natural images that express real-world scenes. They achieve outstanding performance on diverse tasks such as visual question answering. Diagram is a special form of visual expression that frequently appears in the education field and is of great significance for learners to understand multimodal knowledge. Current research on diagrams preliminarily focuses on natural disciplines such as Biology and Geography, whose expressions are still similar to natural images. Another type of diagrams such as from Computer Science is composed of graphics containing complex topologies and relations, and research on this type of diagrams is still blank. The main challenges of graphic diagrams understanding are the rarity of data and the confusion of semantics, which are mainly reflected in the diversity of expressions. In this paper, we construct a novel dataset of graphic diagrams named Computer Science Diagrams (CSDia). It contains more than 1,200 diagrams and exhaustive annotations of objects and relations. Considering the visual noises caused by the various expressions in diagrams, we introduce the topology of diagrams to parse topological structure. After that, we propose Diagram Parsing Net (DPN) to represent the diagram from three branches: topology, visual feature, and text, and apply the model to the diagram classification task to evaluate the ability of diagrams understanding. The results show the effectiveness of the proposed DPN on diagrams understanding.

Einops, an abbreviation of Einstein-Inspired Notation for operations is an open-source python framework for writing deep learning code in a new and better way. Einops provides us with new notation & new operations. It is a flexible and powerful tool to ensure code readability and reliability with minimalist yet powerful API. In case you need convincing arguments for setting aside time to learn about einsum (https://t.co/2lA3Bsh53D) and Alex Rogozhnikov's einops (https://t.co/SY4yJAktEh). Here are a few examples to get started with Einops.

The text message from Billy arrived on students' phones the week of final exams. "It took a lot of hard work, perseverance, and strength to get here, but you've finally made it to the other side -- the end of the semester! I wanted to take a minute and say that I am so proud of you ..." Three emoji hearts concluded the message. "Love you Billy thank you." Heart heart heart. "Thanks Billy, we did it together."

A fingerprint scanner is one of the advanced AI tools used almost everywhere schools, offices, banks, airports where ever we work or have some of our data saved. It is the most forwarding step to link everything that relates to the user and to keep it safe it is locked or secured by the fingerprint. We use smartphones or laptops that are secured by fingerprints that cannot be accessed by others except the user. There is a various tool which keeps the data of the fingerprint through which the scanned pattern is matched and the user get the access of it. It is widely used all over the world not just for securing the data, there is a various system which is a link with the fingerprint scanner. Fingerprint attendance system is used widely in schools, offices to keep the record of the available employee, teacher, and students.

We can think of the humongous field of deep learning as the Earth's crust, floating on a mantle of mathematical and algorithmic understanding. It is a vast sphere of knowledge that is divided into specializations, similar to how tectonic plates divvy up our world. Most important of all, the specializations of deep learning -- natural language processing and cognitive computational science, for instance -- can coincide to form beautiful mountain ranges that help to define landmark areas of deep learning. Curriculum learning is one such Himalayan-range of a deep learning technique between the two fields of AI-oriented cognitive science and NLP. While currently not known to many practitioners or enthusiasts (its Wikipedia page is currently pending approval), for those who choose to explore this hidden gem, the find is worth the time.