Learning Outcomes What you will know

This page collects together all of the “outcomes” associated with individual modules. Outcomes identify what students will know and be able to do if they master the material.

Course-level outcomes

Students mastering the material in this course will achieve the following student learning outcomes for the ICS undergraduate degree program:

[A] Students can apply knowledge of computing and mathematics appropriate to the discipline.
[B] Students can analyze a problem, and identify and define the computing requirements appropriate to its solution.
[C] Students can design, implement, and evaluate a computer-based system, process, component, or program to meet desired needs.

Take aways from EE 645

You understand how things tick in some current state of the art AI and ML techniques
You can formulate problems and take a principled way to solve it
You get a good idea of the connections between machine learning/AI concepts and applications in other courses
You can communicate to a broader audience regarding the utility, risks and guarantees of AI/ML solutions

Referencing modules: Introduction

Linear Models

You understand the basic principles behind linear regression and some classification approaches
You can multiply matrices, and interpret matrix multiplication as linear combinations (both row and column views)
You follow the idea of projections into linear spaces
You understand how Principal Components visualizes data, and interpret eigenvectors/eigenvalues in this fashion

Referencing modules: Soft start: Linear methods

Assessed by: Linear methods