Why should you take this course? Naive Bayes is one of the fundamental algorithms in machine learning, data science, and artificial intelligence. No practitioner is complete without mastering it. This course is designed to be appropriate for all levels of students, whether you are beginner, intermediate, or advanced. You'll learn both the intuition for how Naive Bayes works and how to apply it effectively while accounting for the unique characteristics of the Naive Bayes algorithm.

In these tough times one can learn new skills to get themselves better. How to apply for financial aid for any course? Now you will see 2 main questions and couple of small ones. This part will give you a rough idea of the answers, that I gave. Ans: I am currently an active student from India, so I am investing all my resources, from money (mostly sponsored by my parents) to all of my energy into my study.

A college professor in South Carolina is warning of a new form of academic dishonesty after catching a student using ChatGPT, a new artificial intelligence chat bot that can quickly digest and spit out written information about a vast array of subjects, to write an essay for his philosophy class. The weeks-old technology, released by OpenAI and readily available to the public, comes as yet another blow to higher learning, already plagued by rampant cheating. Darren Hick, an assistant professor of philosophy at Furman University in South Carolina, is sounding the alarm after a student attempted to pass off the relatively sophisticated work of new artificial intelligence chat bot ChatGPT as their own.

As a professor and someone who values academic integrity, I am always concerned about the issue of plagiarism. Plagiarism is the act of using someone else's work or ideas as your own, without proper attribution or citation. It is a serious offense in the academic world, as it undermines the fundamental principle of intellectual honesty and undermines the integrity of the research and scholarship that is being produced. Recently, there has been some concern about the use of AI language models, such as ChatGPT, in the context of plagiarism. ChatGPT is a powerful tool that can generate human-like text, and it has been used for a variety of purposes, including generating chatbot responses and creating content for websites and social media.

Since I began teaching, I have only given essay exams. Rather, each exam has two, complex issue-spotter essay questions. The exam is completely open-book. I always tell my students they can bring whatever they want to the classroom–nothing will help them. I also issue a regular warning: do not cheat, because I will spot similarities in writing very quickly.

Yes, AI can help with programming, but ChatGPT is not ready to be your programming buddy, especially regarding securing your code. Wouldn't it be great to have an AI pair programming friend to help you secure your code? But, while GitHub CoPilot can be handy -- leaving aside whether it's ethical or legal -- AI's new darling chatbot, ChatGPT, isn't ready for programming prime-time. I'll give you that ChatGPT is going to make life much harder for high-school English teachers. Going forward, anyone who assigns a homework paper on To Kill a Mockingbird will be much more likely to get an AI-written document than any real student thought about the literary masterpiece. But programming, especially secure programming, that's another story.

In robotics and computer vision communities, extensive studies have been widely conducted regarding surveillance tasks, including human detection, tracking, and motion recognition with a camera. Additionally, deep learning algorithms are widely utilized in the aforementioned tasks as in other computer vision tasks. Existing public datasets are insufficient to develop learning-based methods that handle various surveillance for outdoor and extreme situations such as harsh weather and low illuminance conditions. Therefore, we introduce a new large-scale outdoor surveillance dataset named eXtremely large-scale Multi-modAl Sensor dataset (X-MAS) containing more than 500,000 image pairs and the first-person view data annotated by well-trained annotators. Moreover, a single pair contains multi-modal data (e.g. an IR image, an RGB image, a thermal image, a depth image, and a LiDAR scan). This is the first large-scale first-person view outdoor multi-modal dataset focusing on surveillance tasks to the best of our knowledge. We present an overview of the proposed dataset with statistics and present methods of exploiting our dataset with deep learning-based algorithms. The latest information on the dataset and our study are available at https://github.com/lge-robot-navi, and the dataset will be available for download through a server.

Explainable AI transforms opaque decision strategies of ML models into explanations that are interpretable by the user, for example, identifying the contribution of each input feature to the prediction at hand. Such explanations, however, entangle the potentially multiple factors that enter into the overall complex decision strategy. We propose to disentangle explanations by finding relevant subspaces in activation space that can be mapped to more abstract human-understandable concepts and enable a joint attribution on concepts and input features. To automatically extract the desired representation, we propose new subspace analysis formulations that extend the principle of PCA and subspace analysis to explanations. These novel analyses, which we call principal relevant component analysis (PRCA) and disentangled relevant subspace analysis (DRSA), optimize relevance of projected activations rather than the more traditional variance or kurtosis. This enables a much stronger focus on subspaces that are truly relevant for the prediction and the explanation, in particular, ignoring activations or concepts to which the prediction model is invariant. Our approach is general enough to work alongside common attribution techniques such as Shapley Value, Integrated Gradients, or LRP. Our proposed methods show to be practically useful and compare favorably to the state of the art as demonstrated on benchmarks and three use cases.

The traditional statistical inference is static, in the sense that the estimate of the quantity of interest does not affect the future evolution of the quantity. In some sequential estimation problems however, the future values of the quantity to be estimated depend on the estimate of its current value. This type of estimation problems has been formulated as the dynamic inference problem. In this work, we formulate the Bayesian learning problem for dynamic inference, where the unknown quantity-generation model is assumed to be randomly drawn according to a random model parameter. We derive the optimal Bayesian learning rules, both offline and online, to minimize the inference loss. Moreover, learning for dynamic inference can serve as a meta problem, such that all familiar machine learning problems, including supervised learning, imitation learning and reinforcement learning, can be cast as its special cases or variants. Gaining a good understanding of this unifying meta problem thus sheds light on a broad spectrum of machine learning problems as well.

In this paper we derive a PAC-Bayesian-Like error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (stochastic LTI systems for short). This class of systems is widely used in control engineering and econometrics, in particular, they represent a special case of recurrent neural networks. In this paper we 1) formalize the learning problem for stochastic LTI systems with inputs, 2) derive a PAC-Bayesian-Like error bound for such systems, 3) discuss various consequences of this error bound.