To read the news, the sanctity of everything from college application essays to graduate school tests to medical licensing exams is imperiled by easy access to advanced artificial intelligence like ChatGPT, the AI chatbot that can produce remarkably clear, long-form answers to complex questions. Educators in particular worry about students turning to ChatGPT to help them complete assignments. One proposed solution is to roll back the clock to the 20th century, making students write exam essays using pen and paper, without the use of any Internet-connected electronic devices. The University of California, Los Angeles, where I teach, is considering making it an honor code violation to use ChatGPT for taking an exam or writing a paper. This semester, I am telling the students in my class at the UCLA School of Law that they are free to use ChatGPT in their writing assignments.

The Central Board of Secondary Education (CBSE) has officially banned the use of ChatGPT, a language processing artificial intelligence (AI) model that has taken the world by storm since its launch in November last year. According to the latest instructions issued by the board ahead of the examinations, "Mobile, ChatGPT and other electronic items will not be allowed in examination hall." "Students are not allowed to carry any electronic devices inside the exam centre. This includes using device to access ChatGPT so that unfair means is not used," a senior board official said. Students at their institutes are banned from using the chatbot for academic assignments such as exams, labs and home works. Launched by Microsoft-back AI research firm, OpenAI in November 2022, ChatGPT is a language processing AI model that is capable of generating human-like text and uses a reinforcement learning method.

Learning this fabulous programming language is not just mandatory to start your journey in machine learning. Still, it is an investment in yourself that you may need all your life because you can even shift your career to another one and still use python in that new industry. This is almost the most popular course among python developers which will help you learn the basics of this language and use the Python built-in data structure, accessing the web, which will be very useful when you are trying to get the data from the web, and using python with the database. The course has more than a million students with a 4.8 rating score which is an excellent resource. Alternatively, you can start your Machine Learning Career with R programming language.

Warning, repository has been renamed to represent its current status. This course aims to introduce students to modern state of Machine Learning and Artificial Intelligence. It is designed to take one full year - approximately 2 * 15 lectures and seminars. All learning materials are available here, full list of topics considered in the course are listed in program_*.pdf Although if you don't have any of this, you could substitude it with your diligence because the course provides additional materials to study requirements yourself.

Variational inequalities are a broad and flexible class of problems that includes minimization, saddle point, fixed point problems as special cases. Therefore, variational inequalities are used in a variety of applications ranging from equilibrium search to adversarial learning. Today's realities with the increasing size of data and models demand parallel and distributed computing for real-world machine learning problems, most of which can be represented as variational inequalities. Meanwhile, most distributed approaches has a significant bottleneck - the cost of communications. The three main techniques to reduce both the total number of communication rounds and the cost of one such round are the use of similarity of local functions, compression of transmitted information and local updates. In this paper, we combine all these approaches. Such a triple synergy did not exist before for variational inequalities and saddle problems, nor even for minimization problems. The methods presented in this paper have the best theoretical guarantees of communication complexity and are significantly ahead of other methods for distributed variational inequalities. The theoretical results are confirmed by adversarial learning experiments on synthetic and real datasets.

Mathematical language in scientific communications and educational scenarios is important yet relatively understudied compared to natural languages. Recent works on mathematical language focus either on representing stand-alone mathematical expressions, especially in their natural tree format, or mathematical reasoning in pre-trained natural language models. Existing works on jointly modeling and generating natural and mathematical languages simply treat mathematical expressions as text, without accounting for the rigid structural properties of mathematical expressions. In this paper, we propose a series of modifications to existing language models to jointly represent and generate text and math: representing mathematical expressions as sequences of node tokens in their operator tree format, using math symbol and tree position embeddings to preserve the semantic and structural properties of mathematical expressions, and using a constrained decoding method to generate mathematically valid expressions. We ground our modifications in GPT-2, resulting in a model MathGPT, and demonstrate that it outperforms baselines on mathematical expression generation tasks.

We study the problem of uncertainty quantification via prediction sets, in an online setting where the data distribution may vary arbitrarily over time. Recent work develops online conformal prediction techniques that leverage regret minimization algorithms from the online learning literature to learn prediction sets with approximately valid coverage and small regret. However, standard regret minimization could be insufficient for handling changing environments, where performance guarantees may be desired not only over the full time horizon but also in all (sub-)intervals of time. We develop new online conformal prediction methods that minimize the strongly adaptive regret, which measures the worst-case regret over all intervals of a fixed length. We prove that our methods achieve near-optimal strongly adaptive regret for all interval lengths simultaneously, and approximately valid coverage. Experiments show that our methods consistently obtain better coverage and smaller prediction sets than existing methods on real-world tasks, such as time series forecasting and image classification under distribution shift.

With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world. Unfortunately, graphs usually suffer from being absent due to privacy-protecting policies or copyright restrictions during data collection. The absence of graph data can be roughly categorized into attribute-incomplete and attribute-missing circumstances. Specifically, attribute-incomplete indicates that a part of the attribute vectors of all nodes are incomplete, while attribute-missing indicates that the whole attribute vectors of partial nodes are missing. Although many efforts have been devoted, none of them is custom-designed for a common situation where both types of graph data absence exist simultaneously. To fill this gap, we develop a novel network termed Revisiting Initializing Then Refining (RITR), where we complete both attribute-incomplete and attribute-missing samples under the guidance of a novel initializing-then-refining imputation criterion. Specifically, to complete attribute-incomplete samples, we first initialize the incomplete attributes using Gaussian noise before network learning, and then introduce a structure-attribute consistency constraint to refine incomplete values by approximating a structure-attribute correlation matrix to a high-order structural matrix. To complete attribute-missing samples, we first adopt structure embeddings of attribute-missing samples as the embedding initialization, and then refine these initial values by adaptively aggregating the reliable information of attribute-incomplete samples according to a dynamic affinity structure. To the best of our knowledge, this newly designed method is the first unsupervised framework dedicated to handling hybrid-absent graphs. Extensive experiments on four datasets have verified that our methods consistently outperform existing state-of-the-art competitors.

Knowledge tracing (KT) serves as a primary part of intelligent education systems. Most current KTs either rely on expert judgments or only exploit a single network structure, which affects the full expression of learning features. To adequately mine features of students' learning process, Deep Knowledge Tracing Based on Spatial and Temporal Deep Representation Learning for Learning Performance Prediction (DKT-STDRL) is proposed in this paper. DKT-STDRL extracts spatial features from students' learning history sequence, and then further extracts temporal features to extract deeper hidden information. Specifically, firstly, the DKT-STDRL model uses CNN to extract the spatial feature information of students' exercise sequences. Then, the spatial features are connected with the original students' exercise features as joint learning features. Then, the joint features are input into the BiLSTM part. Finally, the BiLSTM part extracts the temporal features from the joint learning features to obtain the prediction information of whether the students answer correctly at the next time step. Experiments on the public education datasets ASSISTment2009, ASSISTment2015, Synthetic-5, ASSISTchall, and Statics2011 prove that DKT-STDRL can achieve better prediction effects than DKT and CKT.

The increasing use of Machine Learning (ML) software can lead to unfair and unethical decisions, thus fairness bugs in software are becoming a growing concern. Addressing these fairness bugs often involves sacrificing ML performance, such as accuracy. To address this issue, we present a novel counterfactual approach that uses counterfactual thinking to tackle the root causes of bias in ML software. In addition, our approach combines models optimized for both performance and fairness, resulting in an optimal solution in both aspects. We conducted a thorough evaluation of our approach on 10 benchmark tasks using a combination of 5 performance metrics, 3 fairness metrics, and 15 measurement scenarios, all applied to 8 real-world datasets. The conducted extensive evaluations show that the proposed method significantly improves the fairness of ML software while maintaining competitive performance, outperforming state-of-the-art solutions in 84.6% of overall cases based on a recent benchmarking tool.