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In this work, we explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the University of Amsterdam, which teaches FACT-AI concepts through the lens of reproducibility. The focal point of the course is a group project based on reproducing existing FACT-AI algorithms from top AI conferences and writing a corresponding report. In the first iteration of the course, we created an open source repository with the code implementations from the group projects. In the second iteration, we encouraged students to submit their group projects to the Machine Learning Reproducibility Challenge, resulting in 9 reports from our course being accepted for publication in the ReScience journal. We reflect on our experience teaching the course over two years, where one year coincided with a global pandemic, and propose guidelines for teaching FACT-AI through reproducibility in graduate-level AI study programs. We hope this can be a useful resource for instructors who want to set up similar courses in the future.

Python's popularity is growing tremendously and it's becoming more and more relevant economically and technologically. In this 7 in 1 version you get a full collection of The Python Bible series. From the first volume on, you will be lead on a structured way to the mastery of Python. Besides the basics and the intermediate concepts, you will also learn how to apply it in areas like machine learning, financial analysis and neural networks. At the end you will additionally be introduced to one of the most interesting fields of computer science, which is computer vision After reading this collection, you will not only understand the programming language but you will also be able to work on projects in the stated fields.

Artificial Intelligence has made a huge impact across industries and education is one of them. While on one hand, it has transformed the way schools and teachers perform their job, on the other hand, it has advanced the way students study. As per Market Research Engine, AI in education market will soon pass $5.80 billion in the coming three to four years at a development rate of 45%. How is the education industry getting revolutionized by AI? AI in the education sector has automated administrative operations and made the tasks of companies and professors simpler. Apart from handling the classrooms, teachers also had to deal with several administrative and organizational errands.

Artificial Intelligence has made a huge impact across industries and education is one of them. While on one hand, it has transformed the way schools and teachers perform their job, on the other hand, it has advanced the way students study. As per Market Research Engine, AI in education market will soon pass $5.80 billion in the coming three to four years at a development rate of 45%. How is the education industry getting revolutionized by AI? AI in the education sector has automated administrative operations and made the tasks of companies and professors simpler. Apart from handling the classrooms, teachers also had to deal with several administrative and organizational errands.

Throughout the evolution of humankind, we have always aimed to achieve things that make our life easier by freeing us from laborious tasks. Just take, for example, the invention of the wheel, which revolutionized how we transport things, then thousands of years later came the computer and the internet. Every generation, we are making progress with one ultimate aim: to make our lives easier. So at this age, we are at the threshold where Artificial Intelligence (AI) is taking over our lives and reducing manual labor by leaps and bounds. We have used AI to crunch numbers and solve problems from behind the screen to advance in defense technologies and space exploration.

Modern statistical applications often involve minimizing an objective function that may be nonsmooth and/or nonconvex. This paper focuses on a broad Bregman-surrogate algorithm framework including the local linear approximation, mirror descent, iterative thresholding, DC programming and many others as particular instances. The recharacterization via generalized Bregman functions enables us to construct suitable error measures and establish global convergence rates for nonconvex and nonsmooth objectives in possibly high dimensions. For sparse learning problems with a composite objective, under some regularity conditions, the obtained estimators as the surrogate's fixed points, though not necessarily local minimizers, enjoy provable statistical guarantees, and the sequence of iterates can be shown to approach the statistical truth within the desired accuracy geometrically fast. The paper also studies how to design adaptive momentum based accelerations without assuming convexity or smoothness by carefully controlling stepsize and relaxation parameters.

The lifelong learning paradigm in machine learning is an attractive alternative to the more prominent isolated learning scheme not only due to its resemblance to biological learning, but also its potential to reduce energy waste by obviating excessive model re-training. A key challenge to this paradigm is the phenomenon of catastrophic forgetting. With the increasing popularity and success of pre-trained models in machine learning, we pose the question: What role does pre-training play in lifelong learning, specifically with respect to catastrophic forgetting? We investigate existing methods in the context of large, pre-trained models and evaluate their performance on a variety of text and image classification tasks, including a large-scale study using a novel dataset of 15 diverse NLP tasks. Across all settings, we observe that generic pre-training implicitly alleviates the effects of catastrophic forgetting when learning multiple tasks sequentially compared to randomly initialized models. We then further investigate why pre-training alleviates forgetting in this setting. We study this phenomenon by analyzing the loss landscape, finding that pre-trained weights appear to ease forgetting by leading to wider minima. Based on this insight, we propose jointly optimizing for current task loss and loss basin sharpness in order to explicitly encourage wider basins during sequential fine-tuning. We show that this optimization approach leads to performance comparable to the state-of-the-art in task-sequential continual learning across multiple settings, without retaining a memory that scales in size with the number of tasks. The contemporary machine learning paradigm concentrates on isolated learning (Chen & Liu, 2018) i.e., learning a model from scratch for every new task. In contrast, the lifelong learning (LL) paradigm (Thrun, 1996) defines a biologically-inspired learning approach where models learn tasks in sequence, ideally preserving past knowledge and leveraging it to efficiently learn new tasks. LL has the added benefit of avoiding periodical re-training of models from scratch to learn novel tasks or adapt to new data, with the potential to reduce both computational and energy requirements (Hazelwood et al., 2018; Strubell et al., 2019; Schwartz et al., 2020). In the context of modern machine learning where state-of-the-art models are powered by deep neural networks, catastrophic forgetting has been identified as a key challenge to implementing successful LL systems (McCloskey & Cohen, 1989; French, 1999). Catastrophic forgetting happens when the model forgets knowledge learned in previous tasks as information relevant to the current task is incorporated.

This course will teach you the core fundamentals of financial engineering, with a machine learning twist. We will cover must-know topics in ... Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering? Today, you can stop imagining, and start doing. This course will teach you the core fundamentals of financial engineering, with a machine learning twist. We will learn about the greatest flub made in the past decade by marketers posing as "machine learning experts" who promise to teach unsuspecting students how to "predict stock prices with LSTMs".

Stanford University, CS230 is a widely revered course to learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. Students learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. In the video lecture below, Andrew Ng Adjunct Professor, Computer Science, presents Lecture 8 which touches on career advice and also tips for reading research papers.