Basic Information
Machine Learning Systems Are Increasingly Being Deployed in Production Environments, From Cloud Servers to Mobile Devices. This Course Will Focus On Challenges Inherent to Engineering Machine Learning Systems to Be Correct, Robust, and Fast. Assignments Will Be Project Focused, With Students Building and Deploying Systems For Applications Such As Text Analysis and Recommendation Systems. in Addition to Machine Learning Models, Practical Topics Will Include# Tensor Languages and Auto-differentiation, Model Debugging, Testing, And Visualization, Compression And Low-power Inference. Guest Lectures Will Cover Current Topics From Local Ml Engineers. Learning Outcomes# 1. Mastery of The Key Algorithms For Training and Executing Core Machine Learning Methods. 2. Understanding of The Computational Requirements of Running These Systems. 3. Practical Ability to Debug, Optimize, and Tune Existing Models In Production Environments. 4. Skills to Develop Frontends to Easily Interact With and Explain Predictive Systems. 5. Understanding How Bias Can Be Propagated and Magnified By Ml Systems. 6. Facility to Compare.