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.
Description
TL; DR: Applied machine learning with Python for beginners.
The goal of this course is to ignite a passion for machine learning by demystifying its concepts and showcasing its transformative potential. Students will embark on a journey from understanding the basic principles of machine learning to mastering some of its most powerful algorithms. The course begins with an exploration of the history and wide-ranging applications of machine learning, setting the stage for a deeper dive into the mechanics of machine learning. Along the way, students will engage in hands-on assignments that demonstrate the real-world impact of machine learning. By the end of the course, students will not only have a strong foundational knowledge but also the confidence and curiosity to pursue further studies and innovations in this dynamic field.
Topics
- Machine learning concepts and fundamentals
- Data preprocessing
- Supervised learning: k-nearest neighbors, support vector machine, linear regression, logistic regression, and so on
- Unsupervised learning: k-means, hierarchical clustering, PCA, and so on
- Ensemble learning: random forest, gradient boosting
- Recommendation systems
- Trustworthy machine learning
Prerequisite
- Basic probability and statistics
- Understand the basics of probabilities, gaussian distributions, mean, standard deviation, etc.
- Basic calculus, linear algebra, etc.
- Be comfortable taking derivatives and understanding matrix/vector notation and operations. (e.g., matrix multiplication).
- Basic Python programming skills
- Python, Numpy, and Pandas.
- Watch the video Python for Beginners and read Google's Python Introduction.
- Watch NumPy Tutorial and Pandas Tutorial.
Course Materials
No textbooks are required for this course. I recommend the following books for after-class reading:
- Andreas Muller et al. (2016). Introduction to Machine Learning with Python
Grading for ECE 4420
- Homework
- 5 programming assignments
- In-class quizzes
- Final project
- Final exam
- Attendance
Grading for 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 (East 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 immediately.