Machine Learning Foundations: A Case Study Approach
In this course, students will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course stuednts will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, students are be able to apply machine learning methods in a wide range of domains.
This course treats the machine learning method as a black box. Using this abstraction, students focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, students will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which will be used in developing intelligent applications.
By the end of this course, students will be able to:
- Identify potential applications of machine learning in practice.
- Describe the core differences in analyses enabled by regression, classification, and clustering.
- Select the appropriate machine learning task for a potential application.
- Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
- Represent data as features to serve as input to machine learning models.
- Assess the model quality in terms of relevant error metrics for each task.
- Utilize a dataset to fit a model to analyze new data.
- Build an end-to-end application that uses machine learning at its core.
- Implement these techniques in Python.