Machine Learning
Supervised learning, classification, regression, clustering, evaluation, and model comparison.
Teaching & mentoring
Helping students move from code that runs to ideas they understand.
I enjoy teaching machine learning, artificial intelligence, Python, data science, algorithms, and research workflows through clear examples and hands-on reasoning.
Philosophy
Good teaching makes invisible reasoning visible.
In technical subjects, students often get stuck between mathematical notation, code implementation, and conceptual understanding. I try to connect these layers so that a student can explain what the code is doing, why the algorithm works, and how to verify the output.
My teaching style is practical and feedback-oriented. I value clear working, reproducible code, step-by-step reasoning, and visual checks wherever possible.
For machine learning and AI, I encourage students to move beyond simply running notebooks and toward understanding assumptions, inputs, outputs, evaluation metrics, and failure modes.
Teaching areas
Topics I can teach, support, or mentor.
Artificial Intelligence
Search, reasoning, graph algorithms, intelligent systems, and applied AI workflows.
Python Programming
Scientific Python, notebooks, debugging, NumPy, pandas, plotting, and reproducibility.
Data Science
Data preparation, exploratory analysis, metrics, visualisation, and communicating findings.
Graphs & Algorithms
BFS, DFS, shortest paths, adjacency matrices, traversal order, and implementation details.
Research Computing
Slurm workflows, job scripts, file audits, logs, scientific pipelines, and HPC debugging.
Feedback approach
Clear, useful feedback beats vague correction.
01
Check correctness
I first check whether the answer, code, output, or reasoning is technically correct.
02
Identify the misconception
I try to point out the specific conceptual or implementation issue, not just mark it wrong.
03
Suggest how to improve
I give actionable advice: show working, verify output, plot results, or implement rather than do manually.
Example teaching principle: “The code running is not the same as the method being understood.”
Sample learning materials
Placeholder cards for future resources.
Teaching
Want to collaborate on learning material or workshops?
I’m interested in clear, practical technical education around AI, ML, scientific Python, and research computing.
Contact me