We achieved an F1 score (harmonic mean of precision and recall) of 0.68, which is quite decent considering the limited size of our dataset. My role was related to hyperparameter optimization for the LRCN model, wherein I experimented with different values of the learning rate, dropout, and regularization techniques and how they impacted the results of our model. One important take from the entire experience was how teamwork is crucial to produce an efficient output. The internship was rigorous, with early morning lectures and late night team meetings, but I learned a lot and had fun in the process!

The rapid rise of digitalization and technological innovation is transforming almost every industry domain, including healthcare, banking, & finance. Regarding the revolutionary impact of AI integration upon industrial growth, education is no exception. The rapid incorporation of state-of-the-art technologies like artificial intelligence into education is changing how we learn and teach. AI for education is being used by educational platforms worldwide to create highly collaborative and interactive learning environments for students. AI has many key benefits for education, including personalized learning, digital lessons that are highly immersive, 24/7 access, instant feedback, and improved engagement for students with disabilities. Emergen Research estimates that the global market for artificial intelligence in the education sector accounted for USD 1.55 billion in 2020.

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Markov chains are a class of probabilistic models that have achieved widespread application in the quantitative sciences. This is in part due to their versatility, but is compounded by the ease with which they can be probed analytically. This tutorial provides an in-depth introduction to Markov chains, and explores their connection to graphs and random walks. We utilize tools from linear algebra and graph theory to describe the transition matrices of different types of Markov chains, with a particular focus on exploring properties of the eigenvalues and eigenvectors corresponding to these matrices. The results presented are relevant to a number of methods in machine learning and data mining, which we describe at various stages. Rather than being a novel academic study in its own right, this text presents a collection of known results, together with some new concepts. Moreover, the tutorial focuses on offering intuition to readers rather than formal understanding, and only assumes basic exposure to concepts from linear algebra and probability theory. It is therefore accessible to students and researchers from a wide variety of disciplines.

A maximum of 50 participants are onsite at the Digital Futures hub. The deadline for registration is 10 September! If you cannot participate on-site, you are welcome to join us via Zoom. Zoom-link will be sent upon registration. The main objective of this PhD course, by Dr Pei Wang, Dr Patrick Hammer and Dr Robert Johansson, is to introduce the audience to Artificial General Intelligence.

One type of problem absolutely dominates machine learning and artificial intelligence: classification. Binary classification, the predominant method, sorts data into one of two categories: purchase or not, fraud or not, ill or not, etc. Machine learning and AI-based solutions need accurate, well-chosen algorithms in order to perform classification correctly. This course explains why predictive analytics projects are ultimately classification problems, and how data scientists can choose the right strategy (or strategies) for their projects. Instructor Keith McCormick draws on techniques from both traditional statistics and modern machine learning, revealing their strengths and weaknesses. Keith explains how to define your classification strategy, making it clear that the right choice is often a combination of approaches.

Certified AI & ML BlackBelt Plus Program is the best data science course online to become a globally recognized data scientist. BlackBelt Plus Program includes 105+ detailed (1:1) mentorship sessions, 36 + assignments, 50+ projects, learning 17 Data Science tools including Python, Pytorch, Tableau, Scikit Learn, Power BI, Numpy, Spark, Dask, Feature Tools, Keras,Matplotlib, Rasa, Pandas, ML Box, Scikits-Image, Amazon SageMaker, Streamlit, AWS, Flask, and other technologies such as Computer Vision, Natural Language Processing, Machine Learning, Artificial Intelligence and Deep Learning.

Robots will experience non-stationary environment dynamics throughout their lifetime: the robot dynamics can change due to wear and tear, or its surroundings may change over time. Eventually, the robots should perform well in all of the environment variations it has encountered. At the same time, it should still be able to learn fast in a new environment. We identify two challenges in Reinforcement Learning (RL) under such a lifelong learning setting with off-policy data: first, existing off-policy algorithms struggle with the trade-off between being conservative to maintain good performance in the old environment and learning efficiently in the new environment, despite keeping all the data in the replay buffer. We propose the Offline Distillation Pipeline to break this trade-off by separating the training procedure into an online interaction phase and an offline distillation phase.Second, we find that training with the imbalanced off-policy data from multiple environments across the lifetime creates a significant performance drop. We identify that this performance drop is caused by the combination of the imbalanced quality and size among the datasets which exacerbate the extrapolation error of the Q-function. During the distillation phase, we apply a simple fix to the issue by keeping the policy closer to the behavior policy that generated the data. In the experiments, we demonstrate these two challenges and the proposed solutions with a simulated bipedal robot walk-ing task across various environment changes. We show that the Offline Distillation Pipeline achieves better performance across all the encountered environments without affecting data collection. We also provide a comprehensive empirical study to support our hypothesis on the data imbalance issue.

In present time, the information technology is playing a crucial role in every sector and various advancements are being introduced with every passing day thereby resolving several issues and making work easy for the people. Moreover, with every minute, the technology is upgrading and several new inventions are making place in life. One such thing is Artificial Intelligence, which is not fast emerging and being introduced in every sector. Moreover, looking at the increasing demand and utility of the same in the coming time, the Government is also taking steps for making it in reach of every student, right from the beginning level, giving it a special place in the National Education Policy 2020, which is being implemented across the country. Although the policy, formulated in 2020, got delayed for two years due to COVID pandemic across country but, now, when the situation has improved significantly, efforts are on ensure its implementation across the nation. As per experts, the new education policy will totally transform the existing education system which is aimed for producing clerks to do white-collar jobs, but the New Education Policy has been devised with such an approach, which is aimed at the holistic development of students, that too as per the latest requirements and demands, so that they can transform into responsible citizens of country.

Lecture slide presentations, a sequence of pages that contain text and figures accompanied by speech, are constructed and presented carefully in order to optimally transfer knowledge to students. Previous studies in multimedia and psychology attribute the effectiveness of lecture presentations to their multimodal nature. As a step toward developing AI to aid in student learning as intelligent teacher assistants, we introduce the Multimodal Lecture Presentations dataset as a large-scale benchmark testing the capabilities of machine learning models in multimodal understanding of educational content. Our dataset contains aligned slides and spoken language, for 180+ hours of video and 9000+ slides, with 10 lecturers from various subjects (e.g., computer science, dentistry, biology). We introduce two research tasks which are designed as stepping stones towards AI agents that can explain (automatically captioning a lecture presentation) and illustrate (synthesizing visual figures to accompany spoken explanations) educational content. We provide manual annotations to help implement these two research tasks and evaluate state-of-the-art models on them. Comparing baselines and human student performances, we find that current models struggle in (1) weak crossmodal alignment between slides and spoken text, (2) learning novel visual mediums, (3) technical language, and (4) long-range sequences. Towards addressing this issue, we also introduce PolyViLT, a multimodal transformer trained with a multi-instance learning loss that is more effective than current approaches. We conclude by shedding light on the challenges and opportunities in multimodal understanding of educational presentations.