This seminar will be run via zoom, when you register on Eventbrite you will receive a zoom registration email alongside your standard Eventbrite registration email. Where speakers have given permission to be recorded, their talks will be made available on our AI3SD YouTube Channel. Abstract: Prediction of protein functional properties from sequence is a central challenge that would allow us to discover new proteins with specific functionality. Experimental breakthroughs allow data on the relationship between sequence and function to be rapidly acquired that can be used to train and validate machine learning models that predict protein function directly from sequence. However, the cost and latency of wet-lab experiments require methods that find good sequences in few experimental rounds, where each round contains large batches of sequence designs.

Machine Learning is one of the hottest technology field in the world right now! This field is exploding with opportunities and career prospects. Machine Learning techniques are widely used in several sectors now a days such as banking, healthcare, finance, education transportation and technology. This course covers several technique in a practical manner, the projects include coding sessions as well as Algorithm Intuition: So, if you've ever wanted to play a role in the future of technology development, then here's your chance to get started with Machine Learning. Because in a practical life, machine learning seems to be complex and tough,thats why we've designed a course to help break it down into real world use-cases that are easier to understand.

Potts is a doctoral candidate in the Department of Mathematical Sciences who studies the development of machine learning and data science …

If you have access to Oracle Database, then you probably already have access to Oracle Machine Learning (OML). In this hands-on workshop based on Autonomous Database, you will learn how to use OML to build a machine learning model and integrate it into an APEX application. The workshop is suitable for Oracle data professionals looking to build their first machine learning model, and data scientists who want the simplest way to apply machine learning to enterprise data.

Artificial Intelligence for Simple Games - Learn how to use powerful Deep Reinforcement Learning and Artificial Intelligence tools on examples of AI simple games! Created by Jan Warchocki, Ligency TeamPreview this Course - GET COUPON CODE Ever wish you could harness the power of Deep Learning and Machine Learning to craft intelligent bots built for gaming? If you're looking for a creative way to dive into Artificial Intelligence, then'Artificial Intelligence for Simple Games' is your key to building lasting knowledge. Learn and test your AI knowledge of fundamental DL and ML algorithms using the fun and flexible environment of simple games such as Snake, the Travelling Salesman problem, mazes and more. Whether you're an absolute beginner or seasoned Machine Learning expert, this course provides a solid foundation of the basic and advanced concepts you need to build AI within a gaming environment and beyond.

Prediction tasks about students have practical significance for both student and college. Making multiple predictions about students is an important part of a smart campus. For instance, predicting whether a student will fail to graduate can alert the student affairs office to take predictive measures to help the student improve his/her academic performance. With the development of information technology in colleges, we can collect digital footprints which encode heterogeneous behaviors continuously. In this paper, we focus on modeling heterogeneous behaviors and making multiple predictions together, since some prediction tasks are related and learning the model for a specific task may have the data sparsity problem. To this end, we propose a variant of LSTM and a soft-attention mechanism. The proposed LSTM is able to learn the student profile-aware representation from heterogeneous behavior sequences. The proposed soft-attention mechanism can dynamically learn different importance degrees of different days for every student. In this way, heterogeneous behaviors can be well modeled. In order to model interactions among multiple prediction tasks, we propose a co-attention mechanism based unit. With the help of the stacked units, we can explicitly control the knowledge transfer among multiple tasks. We design three motivating behavior prediction tasks based on a real-world dataset collected from a college. Qualitative and quantitative experiments on the three prediction tasks have demonstrated the effectiveness of our model.

The Arab region consists of many teaching-intensive universities that are intrinsically committed to holistic educational excellence. According to a recent UNESCO report,5 the higher education sector in the Arab region is undergoing a need for massive expansion given exponentially growing populations, record-breaking youth cohorts, coupled with a strong recognition of the economic and social value of higher education. Such an enormous need for growth poses a significant challenge for publicly funded universities yet offers many opportunities for private universities to meet the ever-increasing demands of advanced education.2 As is the case with many similar universities worldwide, not being dedicated research institutions often results in limited availability of research funds, resources, and hence innovation throughput. The examples given in this paper are those of universities in the region that were initially focused on consolidating their teaching, except for one which started first as research-intensive. However, it was not long before a shift in policy included research excellence in undergraduate education by harnessing the most valuable resource of any university: the aspiring students themselves.

Incorporation of Artificial Emotional Intelligence in E-Learning platforms creates new possibilities in online education.We all know that how everything has changed post-pandemic. Every industry had to find new ways to run their processes to survive during the pandemic and post-pandemic situation. The same implies to the education sector. Still the schools, colleges, and Universities in most of the countries follow virtual classes system as the situation hasn’t saturated. […]

We are teaching a major update of the course Spring 2021 as an official UC Berkeley course and as an online course, with all lectures and labs available for free. There are many great courses to learn how to train deep neural networks. However, training the model is just one part of shipping a deep learning project. The course is aimed at people who already know the basics of deep learning and want to understand the rest of the process of creating production deep learning systems. While we cover the basics of deep learning (backpropagation, convolutional neural networks, recurrent neural networks, transformers, etc), we expect these lectures to be mostly review.

In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.