Varada Kolhatkar

Varada Kolhatkar

Educator, Computational Linguist, Computer Scientist

Teaching

CPSC 330: Applied Machine Learning Permalink

Computer Science, University of British Columbia, 2022

This course provides a broad introduction to applied machine learning. The topics covered include machine learning terminology and fundamentals, preprocessing, sklearn pipelines and column transformers, building supervised machine learning pipelines, introduction to unsupervised machine learning, introduction to specialized fields such as natural language processing, computer vision, time series, survival analysis, communication, responsible use of machine learning technology, and model deployment.

DSCI 563: Unsupervised Learning

Master of Data Science, University of British Columbia, 2021

This course is about identifying underlying structure in data. We will talk about clustering, dimensionality reduction, word embeddings, and recommendation systems.

DSCI 573: Feature and Model Selection Permalink

Master of Data Science, University of British Columbia, 2020

This course is about evaluating and selecting features and models. It covers the following topics: evaluation metrics, feature engineering, feature selection, the role of regularization, loss functions, and feature importances.

DSCI 571: Supervised Machine Learning I Permalink

Master of Data Science, University of British Columbia, 2020

Welcome to DSCI 571, an introductory supervised machine learning course! In this course we will focus on basic machine learning concepts such as data splitting, cross-validation, generalization error, overfitting, the fundamental trade-off, the golden rule, and data preprocessing. You will also be exposed to common machine learning algorithms such as decision trees, K-nearest neighbours, SVMs, naive Bayes, and logistic regression using the scikit-learn framework.

CPSC 322: Introduction to Artificial Intelligence Permalink

Computer Science, University of British Columbia, 2019

This course provides an introduction to the field of artificial intelligence (AI). The major topics covered include reasoning and representation, search, constraint satisfaction problems, planning, logic, reasoning under uncertainty, and planning under uncertainty.