The three courses in this specialization will push the boundaries of the idea of "creative artifacts" in this multidisciplinary field. These courses are designed for those with technical backgrounds who are willing to look at things from a different perspective and for those who work in the creative field and want to better understand A.I. research and its implications in their industries. Applied Learning Project Each course in this specialization has an immersive project that you will conduct throughout the course. This is where you'll engage your passion for understanding the machine learning research and controversies surrounding A.I. and apply it to your current work. This specialization is designed to be different from the general online learning experience. For each project, you will be expected to leave your computer and conduct field research on a topic of your interest. Throughout each project, you will challenge your own definitions of creativity in different ways.

Goto Practical Data Science Specialization Become a Practical Data Science expert. This Specialization 80,604 recent views Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills.

This Specialization 160,486 recent views The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

Recently, neural network compression schemes like channel pruning have been widely used to reduce the model size and computational complexity of deep neural network (DNN) for applications in power-constrained scenarios such as embedded systems. Reinforcement learning (RL)-based auto-pruning has been further proposed to automate the DNN pruning process to avoid expensive hand-crafted work. However, the RL-based pruner involves a time-consuming training process and the high expense of each sample further exacerbates this problem. These impediments have greatly restricted the real-world application of RL-based auto-pruning. Thus, in this paper, we propose an efficient auto-pruning framework which solves this problem by taking advantage of the historical data from the previous auto-pruning process. In our framework, we first boost the convergence of the RL-pruner by transfer learning. Then, an augmented transfer learning scheme is proposed to further speed up the training process by improving the transferability. Finally, an assistant learning process is proposed to improve the sample efficiency of the RL agent. The experiments have shown that our framework can accelerate the auto-pruning process by 1.5-2.5 times for ResNet20, and 1.81-2.375 times for other neural networks like ResNet56, ResNet18, and MobileNet v1.

In this article, you will learn about a real-world example of the use of artificial intelligence in medical imaging. Read on to learn the details of how various deep learning models are combined to analyze images taken with a microscope. You may have read use cases where AI is used in medical diagnosis to differentiate between images showing pathological and non-pathological features (e.g. Capillaroscopy consists of observing the blood capillaries at the base of the patient's nails (nail bed) using a microscope called a capillaroscope and helps to determine the state of the patient's vascular system in a simple, fast and non-invasive way. Capillaroscopy is frequently used for the diagnosis and follow-up of some autoimmune diseases such as scleroderma, dermatomyositis or mixed connective tissue disease.

This Course 10,971 recent views Artificial Creativity is about exploring the emerging field of artificial intelligence (A.I.) from a design perspective with the intent to bring those with a programming background and more "traditional" creatives together. In this course, you will look back at the history and theories behind today's A.I., analyze the unorthodox approaches that have advanced the field, utilize current A.I. tools, and practice design thinking methodologies that can be applied to everyday business decision making. You will examine the potential of creative A.I. in everyday experience. You will implement various design research methodologies through observation, reflective writing and discussion prompts. Then, you will actively engage and collaborate with others in the class while challenging your own definitions of creativity by taking a closer look at the people and projects that have changed the paradigm of what machines can do.

Before I started this MS program, I was looking for course curricula of different Masters programs and trying to find reviews of other people to understand which program is suitable for me. Now, as I am almost done with my MS, I thought I should write a review to help other learners who are looking for an MS program in Data Science or Analytics. Before I dive into the MS program, here is my background. I have a Bachelor's in Civil Engineering and a master's in Environmental Engineering. So, I am not from a computer science background.

This survey article has grown out of the RL4ED workshop organized by the authors at the Educational Data Mining (EDM) 2021 conference. We organized this workshop as part of a community-building effort to bring together researchers and practitioners interested in the broad areas of reinforcement learning (RL) and education (ED). This article aims to provide an overview of the workshop activities and summarize the main research directions in the area of RL for ED.

Learning multimodal representations involves integrating information from multiple heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world applications in multimedia, affective computing, robotics, finance, human-computer interaction, and healthcare. Unfortunately, multimodal research has seen limited resources to study (1) generalization across domains and modalities, (2) complexity during training and inference, and (3) robustness to noisy and missing modalities. In order to accelerate progress towards understudied modalities and tasks while ensuring real-world robustness, we release MultiBench, a systematic and unified large-scale benchmark spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas. MultiBench provides an automated end-to-end machine learning pipeline that simplifies and standardizes data loading, experimental setup, and model evaluation. To enable holistic evaluation, MultiBench offers a comprehensive methodology to assess (1) generalization, (2) time and space complexity, and (3) modality robustness. MultiBench introduces impactful challenges for future research, including scalability to large-scale multimodal datasets and robustness to realistic imperfections. To accompany this benchmark, we also provide a standardized implementation of 20 core approaches in multimodal learning. Simply applying methods proposed in different research areas can improve the state-of-the-art performance on 9/15 datasets. Therefore, MultiBench presents a milestone in unifying disjoint efforts in multimodal research and paves the way towards a better understanding of the capabilities and limitations of multimodal models, all the while ensuring ease of use, accessibility, and reproducibility. MultiBench, our standardized code, and leaderboards are publicly available, will be regularly updated, and welcomes inputs from the community.

In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build effective study habits. This task is rather challenging due to the widely-varying quality and pedagogical styles of dialogic instructions. To address these challenges, we utilize pre-trained language models, and propose a multi-task paradigm which enhances the ability to distinguish instances of different classes by enlarging the margin between categories via contrastive loss. Furthermore, we design a strategy to fully exploit the misclassified examples during the training stage. Extensive experiments on a real-world online educational data set demonstrate that our approach achieves superior performance compared to representative baselines.