TEACHING

I teach university courses and bootcamps, and deliver talks and workshops for corporate teams, professional communities, and technical audiences.

Amazon

Hands-on Machine Learning with Python

Contents:

  • Data structures in NumPy and Pandas

  • Demonstrate machine learning techniques and algorithm

  • Understand supervised learning and unsupervised learning

  • Examine convolutional neural networks and Recurrent neural networks

  • Get acquainted with scikit-learn and PyTorch

  • Predict sequences in recurrent neural networks and long short term memory

BOOK HIGHLIGHT

Springer

Data Science and Society (INFOMDSS)

a person holding a piece of paper over a laptop
a person holding a piece of paper over a laptop
About Course

"I feel like there are few study projects I ever worked on where I learnt so much practical and applicable skills. But I think learning as much from the project as I did really required a strong proactivity and the right amount of having just enough preliminarry knowledge and willingness to delve deeper into matters."

-Student from INFOMDSS 2023

Project Ideas

I believe people learn better when they are personally connected with what they are learning, which leads to intrinsic motivation and strong self-reflection. My teaching connects concepts, systems, use cases, evaluation, and practical decision-making. I help people understand complex technical topics in a simple, structured, and goal-oriented way.

Teaching Philosophy

Abstract architectural geometry, clean concrete lines intersecting under soft morning daylight, casting subtle shadows, pale blue and warm off-white tones.
Abstract architectural geometry, clean concrete lines intersecting under soft morning daylight, casting subtle shadows, pale blue and warm off-white tones.
The Methodology

A human-centered testing framework

General capability benchmarks fail in production. Our proprietary evaluation framework stress-tests generative models against actual human workflows, identifying cognitive friction points and behavioral feedback loops before they impact your bottom line.

By combining applied machine learning with rigorous behavioral science, we translate unpredictable model outputs into structured, deterministic business assets.

OvP Python

If you are an OvP students or an assistant trainer in the on-two day workshop, download the material from the links below. The material might slightly change over the years - if you are looking for solutions, please confirm the material dates and discuss with your trainer or me.

COURSE MATERIAL

Github
Measurement Vectors

Empirical metrics that matter

We replace speculative tech-stack hype with precise, observable indicators of system performance and user alignment.

Alignment Depth

Behavioral Friction

Risk Mitigation

Quantifying how closely generative outputs match domain-specific expertise, policy constraints, and operational guidelines.

Tracking real-time user correction rates, prompt fatigue, and drop-off points within the human-AI interaction loop.

Stress-testing models against edge cases, hallucination thresholds, and systemic biases under simulated real-world pressure.

Discuss Your System

Ready to evaluate your system?

Schedule a strategic briefing to audit your current generative AI deployment pipeline.