Machine learning (ML) systems are increasingly deployed in high-stakes domains where reliability is paramount. This thesis investigates how uncertainty estimation can enhance the safety and trustworthiness of ML, focusing on selective prediction -- where models abstain when confidence is low. We first show that a model's training trajectory contains rich uncertainty signals that can be exploited without altering its architecture or loss. By ensembling predictions from intermediate checkpoints, we propose a lightweight, post-hoc abstention method that works across tasks, avoids the cost of deep ensembles, and achieves state-of-the-art selective prediction performance. Crucially, this approach is fully compatible with differential privacy (DP), allowing us to study how privacy noise affects uncertainty quality. We find that while many methods degrade under DP, our trajectory-based approach remains robust, and we introduce a framework for isolating the privacy-uncertainty trade-off. Next, we then develop a finite-sample decomposition of the selective classification gap -- the deviation from the oracle accuracy-coverage curve -- identifying five interpretable error sources and clarifying which interventions can close the gap. This explains why calibration alone cannot fix ranking errors, motivating methods that improve uncertainty ordering. Finally, we show that uncertainty signals can be adversarially manipulated to hide errors or deny service while maintaining high accuracy, and we design defenses combining calibration audits with verifiable inference. Together, these contributions advance reliable ML by improving, evaluating, and safeguarding uncertainty estimation, enabling models that not only make accurate predictions -- but also know when to say "I do not know".

This article was made in collaboration with Sean Eugene Chua, an undergraduate student at the University of Toronto who has shared experiences across fields that include data science, programming, and machine learning. By definition, it is when a machine can imitate human behavior and emulate how they think and act. A published paper written by logician Walter Pitts and neuroscientist Warren S. McCulloch entitled "A logical calculus of the ideas immanent in nervous activity" was regarded as a breakthrough in laying the first foundations of machine learning. It indicates the usage of mathematical principles to detail the science and psychology behind human decision-making. However, in 1950, Alan Turing introduced what computer scientists now know of as the "Turing Test" to determine whether a machine can be considered intelligent or unintelligent.

Just getting started in Machine learning? Wondering what material to pick from the flood of literature out there? I have just the right recommendation for you right here. Before we get started some quick info about my background. I studied mechanical engineering a few years ago, then did a PhD in quantitative finance.

The three little bears strived to get it just right, and AI model builders strive to do the same thing when it comes to specifying their model. Underspecification is when you build a model that performs well on your data, but so do other models, which could lead to your model decaying over time. The discussion of underspecification kicked off last fall when Google researchers published a paper on the subject, "Underspecification Presents Challenges for Credibility in Modern Machine Learning." "ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures," stated the paper, put together by a group of scientists led by author Alexander D'Amour, a research scientist with Google Brain of Cambridge, Mass.

I recently started an AI-focused educational newsletter, that already has over 100,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. A common analogy in artificial intelligence(AI) circles is that training data is the new oil for machine learning models. Just like the precious commodity, training data is scarce and hard to get at scale.

Abstract. We present modern machine learning, focusing on the state-of-the-art classification methods of deep networks and decision forests, …

Udemy course Feature Engineering and Dimensionality Reduction Feature Selection vs Dimensionality Reduction While both methods are used for reducing the number of features in a dataset, there is an important difference. Feature selection is simply selecting and excluding given features without changing them. Dimensionality reduction transforms features into a lower dimension NED New What you'll learn The importance of Feature Engineering and Dimensionality Reduction in Data Science. Practical explanation and live coding with Python. Description Artificial Intelligence (AI) is indispensable these days.

It's no secret that mathematics is the foundation of machine learning, and is vital to your understanding of the underpinnings of the field. In order to succeed as a machine learning practitioner, knowledge of the applicable mathematical foundations are absolutely necessary. Where can you turn to brush up on your machine learning maths, or strengthen your understanding by extending that base? Mathematics for Machine Learning is a book currently in development by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong, with the goal of motivating people to learn mathematical concepts, and which is set to be published by Cambridge University Press. According to the authors, the goal of the text is to provide the necessary mathematical skills to subsequently read books on more advanced machine learning topics.

I will go through all these issues. This claim is 100% false because anything you run has a backend in the fastest/one of the fastest languages. Also, just as an example because I write Keras mostly myself, here's how to add more GPUs in Keras. CPUs is a similarly complex operation. What even are the cases where you need to interface with your GPU/CPU in a way that is unsupported by the Tensorflow/Pytorch/Keras library but is absolutely critical for your ML project?

Such feats of work are a testament to modern machine learning. But for all its power, Facebook's world-mapping AI has a severe weakness – it did not understand what it had done. If asked to explain itself, it wouldn't even understand the question. Unlike a human, Facebook's superhuman mapping system is completely incapable of even ordering a cup of coffee, let alone speaking French or shooting hoops. This is typical of modern artificial intelligence, which is built around a technique that apes the human visual system: deep learning.