ECE 4420/6420 Knowledge Engineering

Revised on 08/26/2024

For the definitive and up-to-date course details, please refer to the syllabus on Canvas.

Course Overview

ECE 4420/6420 Knowledge Engineering is an introductory course in applied machine learning using Python. It is designed to ignite a passion for machine learning by demystifying its concepts and showcasing its transformative potential.

Course Description

Students will embark on a journey from understanding the basic principles of machine learning to mastering powerful algorithms. The course begins with an exploration of the history and applications of machine learning, setting the stage for a deeper dive into its mechanics. Hands-on assignments demonstrate the real-world impact of machine learning, equipping students with foundational knowledge and the confidence to pursue further studies in this dynamic field.

Topics Covered

  • Machine learning concepts and fundamentals
  • Data preparation
  • Supervised learning: k-nearest neighbors, support vector machine, linear regression, logistic regression, and more
  • Unsupervised learning: k-means, hierarchical clustering, PCA, and more
  • Ensemble learning: random forest, gradient boosting
  • Recommendation systems
  • Trustworthy machine learning

Prerequisites

  • Basic probability and statistics
  • Understand probabilities, Gaussian distributions, mean, standard deviation, etc.
  • Basic calculus and linear algebra
  • Be comfortable with derivatives and matrix/vector operations (e.g., matrix multiplication).
  • Basic Python programming skills
  • Python, Numpy, and Pandas.
  • Watch Python for Beginners and read Google's Python Introduction.
  • Watch NumPy Tutorial and Pandas Tutorial.

Course Materials

No textbooks are required for this course. Recommended for after-class reading:

Grading Criteria

ECE 4420

  • Homework: 5 programming assignments
  • In-class quizzes
  • Final project
  • Final exam
  • Attendance

ECE 6420

  • Homework: 5 programming assignments
  • In-class quizzes (with additional questions)
  • Final project
  • Final exam
  • Attendance

Late Policy

  • On the due date, the cutoff for on-time submission is 11:59 pm (Eastern Time).
  • Late work is discounted 5% per calendar day late.
  • Late submissions are not accepted after seven calendar days past the original due date and are graded as zero.