Bayesian Machine Learning for Optimization in Python
Learn Bayesian optimization and statistical modeling to tackle high-dimensional problems. Explore hyperparameter tuning, experimental design, algorithm configuration, and system optimization.
I am a doctoral candidate at the University of Vaasa, Finland, under the supervision of Dr. Joni Salminen focusing on Generative AI (GenAI) and user representation research. Currently, I work as an AI Research Scientist at Samsung Design Innovation Center in France, developing GenAI tools that help designers create more representative products. Previously, I have created AI solutions in engineering domains at several orgnanizations in Pakistan, USA, MENA, and Ireland. I have a background in engineering and have earned multiple credentials in data science and business from institutions like MIT, UC San Diego, RIT, Google, and EITCA. My research centers on making GenAI systems fairer and more inclusive towards representation of users. I study how GenAI creates user personas and work to reduce bias, especially for marginalized communities. I use large language models and NLP to build better representations of different groups of people. My goal is ensuring GenAI systems don't perpetuate stereotypes. I also explore how GenAI can be used responsibly in design and decision-making. I speak four languages: English, Urdu, Punjabi, and some (petit :smile) French. I enjoy writing poetry and dark prose. When I am not working, I'm probably overthinking or cooking. I have been involved in space education programs for popularization through Space Technology Education and Popularization , a non-profit organized by me and my colleagues, in Pakistan for many years, helping to organize events such as World Space Week (2014-2022), Space Summer School (2017-2022), and Space Camps all across Pakistan. I volunteer with National Center of GIS & Space Applications for Space and STEM popularization. I enjoy mentoring startups and helping them ethically utilize GenAI.
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What You'll Learn
- An understanding of Bayes’ theorem and its applications
- Familiarity with the core components of Bayesian machine learning and its application to optimization
- Hands-on experience tuning hyperparameters using Bayesian optimization
- The ability to attain optimized solutions for complex problems using Bayesian statistics
- Familiarity with core components of the Dragonfly framework for scalable Bayesian optimization
Learning Roadmap
1.
Introduction to Bayesian Statistics
Introduction to Bayesian Statistics
2.
Bayesian Machine Learning
Bayesian Machine Learning
3.
Optimization: An Overview
Optimization: An Overview
6 Lessons
6 Lessons
4.
Bayesian Optimization: From Scratch
Bayesian Optimization: From Scratch
9 Lessons
9 Lessons
Danial Amin
I am a doctoral candidate at the University of Vaasa, Finland, under the supervision of Dr. Joni Salminen focusing on Generative AI (GenAI) and user representation research. Currently, I work as an AI Research Scientist at Samsung Design Innovation Center.
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