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From Algorithms to Agents: Lessons from Building Copilot 2025-09-15 · 13:00 What Generative AI Has Changed and Which Fundamentals Still Matter - Vadim Smolyakov Vadim’s path spans from algorithmic ML and MIT research to building Copilot at Microsoft, providing him with a front-row view of how practice has shifted from hand-built algorithms to agentic systems. In this conversation, he reflects on what genuinely changed with generative AI and what fundamentals still matter. We’ll discuss assistants as complements to human thinking, why embodiment (“physical AI”) may be the next step, and how goal-setting and sharing knowledge shape work that lasts. We plan to cover: What Copilot taught about scope, trade-offs, and evaluation Agents as assistants: avoiding over-reliance while gaining real leverage Fine-tuning, synthetic data, and how the craft of ML has evolved Physical AI: what to build in vs. what to learn from the environment Purpose, habits, and leaving durable digital/physical artifacts About the speaker Vadim Smolyakov is a Machine Learning Engineer at Microsoft, working on Copilot AI, an author of Machine Learning Algorithms in Depth, and a former MIT PhD student. He focuses on generative AI (agents, RL), self-development, knowledge sharing, and the societal impact of intelligent systems. Join our slack: https://datatalks.club/slack.html	From Algorithms to Agents: Lessons from Building Copilot
Machine Learning Algorithms in Depth 2025-01-27 Vadim Smolyakov – author Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. Fully understanding how machine learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you’ll explore practical implementations of dozens of ML algorithms including: Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimization for Hyperparameter Tuning Dirichlet Process K-Means for Clustering Applications Stock Clusters based on Inverse Covariance Estimation Energy Minimization using Simulated Annealing Image Search based on ResNet Convolutional Neural Network Anomaly Detection in Time-Series using Variational Autoencoders Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probabilistic algorithms, you’ll learn the fundamentals of Bayesian inference and deep learning. You’ll also explore the core data structures and algorithmic paradigms for machine learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they’re put into action. About the Technology Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods. About the Book Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You’ll especially appreciate author Vadim Smolyakov’s clear interpretations of Bayesian algorithms for Monte Carlo and Markov models. What's Inside Monte Carlo stock price simulation EM algorithm for hidden Markov models Imbalanced learning, active learning, and ensemble learning Bayesian optimization for hyperparameter tuning Anomaly detection in time-series About the Reader For machine learning practitioners familiar with linear algebra, probability, and basic calculus. About the Author Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft. Quotes I love this book! It shows you how to implement common ML algorithms in plain Python with only the essential libraries, so you can see how the computation and math works in practice. - Junpeng Lao, Senior Data Scientist at Google I highly recommend this book. In the era of ChatGPT real knowledge of algorithms is invaluable. - Vatsal Desai, InfoDesk Explains algorithms so well that even a novice can digest it. - Harsh Raval, Zymr data ai-ml machine-learning AI/ML LLM Microsoft Monte Carlo NLP Python Cyber Security	O'Reilly AI & ML Books

What Generative AI Has Changed and Which Fundamentals Still Matter - Vadim Smolyakov

Vadim’s path spans from algorithmic ML and MIT research to building Copilot at Microsoft, providing him with a front-row view of how practice has shifted from hand-built algorithms to agentic systems.

In this conversation, he reflects on what genuinely changed with generative AI and what fundamentals still matter.

We’ll discuss assistants as complements to human thinking, why embodiment (“physical AI”) may be the next step, and how goal-setting and sharing knowledge shape work that lasts.

We plan to cover:

What Copilot taught about scope, trade-offs, and evaluation
Agents as assistants: avoiding over-reliance while gaining real leverage
Fine-tuning, synthetic data, and how the craft of ML has evolved
Physical AI: what to build in vs. what to learn from the environment
Purpose, habits, and leaving durable digital/physical artifacts

About the speaker

Vadim Smolyakov is a Machine Learning Engineer at Microsoft, working on Copilot AI, an author of Machine Learning Algorithms in Depth, and a former MIT PhD student. He focuses on generative AI (agents, RL), self-development, knowledge sharing, and the societal impact of intelligent systems.

Join our slack: https://datatalks.club/slack.html

Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. Fully understanding how machine learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you’ll explore practical implementations of dozens of ML algorithms including: Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimization for Hyperparameter Tuning Dirichlet Process K-Means for Clustering Applications Stock Clusters based on Inverse Covariance Estimation Energy Minimization using Simulated Annealing Image Search based on ResNet Convolutional Neural Network Anomaly Detection in Time-Series using Variational Autoencoders Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probabilistic algorithms, you’ll learn the fundamentals of Bayesian inference and deep learning. You’ll also explore the core data structures and algorithmic paradigms for machine learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they’re put into action. About the Technology Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods. About the Book Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You’ll especially appreciate author Vadim Smolyakov’s clear interpretations of Bayesian algorithms for Monte Carlo and Markov models. What's Inside Monte Carlo stock price simulation EM algorithm for hidden Markov models Imbalanced learning, active learning, and ensemble learning Bayesian optimization for hyperparameter tuning Anomaly detection in time-series About the Reader For machine learning practitioners familiar with linear algebra, probability, and basic calculus. About the Author Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft. Quotes I love this book! It shows you how to implement common ML algorithms in plain Python with only the essential libraries, so you can see how the computation and math works in practice. - Junpeng Lao, Senior Data Scientist at Google I highly recommend this book. In the era of ChatGPT real knowledge of algorithms is invaluable. - Vatsal Desai, InfoDesk Explains algorithms so well that even a novice can digest it. - Harsh Raval, Zymr

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