Robin Yancey

Hi! My name is Robin. I have PhD and MS degrees in Computer Science and Electrical Engineering from UC Davis, where I was employed as the full instructor for the undergraduate Machine Learning Course. Afterwards, I completed a Machine Learning PostDoc at LLNL working on NIF during the recent world breakthrough achieving nuclear fusion. Throughout my university and postdoc research I not only published dozens of papers, but also developed many popular frameworks in Machine Learning, Deep Learning, Computer Vision, and Data Science.

I do any type of mentoring/consulting (eg. PhD research projects, industry); ask me what you need help with! I also offer 3 different series of lessons (Python, ML, and DL). Some topics covered include the following:

Machine Learning:
- KNN, Decision Trees (including random forest, bagging, boosting), PCA, SVM, Linear and Polynomial Models (including GLM), Neural Networks (all both code in sklearn and mathematical theory)
- Prepare data for ML using Pandas (handle NAs, missing values, data type issues etc.)
- Core concepts: classification/regression, dimensionality reduction, over/under-fitting, k-fold testing, proper results/accuracy presentation, parallelization, hyper-parameter optimization, class imbalance, scaling and handling outliers

Deep Learning:
- Apply DL models in PyTorch, Tensorflow, Keras, and Scikit-Learn
- Build full train/test/eval frameworks 
- Understand how to choose loss functions, optimizers, and other DL hyperparameters
- Transfer Learning (and how to retrain or fine-tune off-the-shelf top models for other tasks)
- CNN, Transformers architecture breakdown and how to implement (eg. ViT, Swin, HuggingFace models etc.)
- Image classification, object detection, NLP
- GPU training, handling large tensor data, handling class imbalance
- Data transformations, augmentation
- Results analysis/reporting/plotting 

Python:
- Basics
- LeetCode problems and Interview prep (eg. Trees, Linked Lists, time-complexity, sorting)