Prof. Angelique Taylor
- Office hours: Mondays from 2:15PM-3:15PM, Bloomberg 61X
- Email: email@example.com
- Teaching Assistant
- Office hours: TBD
- Email: firstname.lastname@example.org
- Email: email@example.com
This course provides hands-on experience developing and deploying foundational machine learning algorithms on real-world datasets for practical applications (e.g., healthcare, computer vision). Students will learn about the machine learning pipeline end-to-end including dataset creation, pre- and post-processing, annotation, annotation validation, preparation for machine learning, training and testing a model, and evaluation. Students will focus on real-world challenges at each stage of the ML pipeline while handling bias in models and datasets. Lastly, students will analyze the strengths and weaknesses of regression, classification, clustering, and deep learning algorithms.
- Collect a new dataset and prepare it for a ML task, train a model, and evaluate it
- Apply regression, classification, clustering, and deep learning algorithms to practical applications
- Analyze and identify key differences in regression, classification, clustering, and deep learning algorithms
- Understand core challenges of dataset creation including handling missing data, bias, unlabeled data, among others
- Represent features in datasets to be used for ML tasks
- Evaluate model quality using appropriate metrics of performance
- Lectures – Monday and Wednesday 1:00PM – 2:25PM ET, Bloomberg 61X
- Student Lightning Talks –
- Guest Lectures –
- Ameisen, Emmanuel. Building Machine Learning Powered Applications: Going from Idea to Product. ” O’Reilly Media, Inc.”, 2020.
- Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. ” O’Reilly Media, Inc.”, 2022.
- Jigyasa Grover, Rishabh Misra, Julian McAuley, Laurence Moroney, Mengting Wan (Foreword)“Sculpting Data for ML: The first act of Machine Learning”
Libraries and Tools
Summary of Course Topics
Students are expected to treat their classmates and course staff with respect. All individuals from different cultural backgrounds, genders, and sexual orientations are welcome here. When students encounter incidents that violate this, they are encouraged to inform the instructors so these issues can be addressed in a timely manner (See Cornell’s Computer Science Community Statement of Values of Inclusion).
We are happy to accommodate all students in terms of accessibility. Please contact the course instructors when you need help. Furthermore, the Office of Student Disability Services has available resources.