Machine Learning | Dr Kasım Zor

EEE435 - Machine Learning (2024-2025 Spring)

Course Details

Venue: D204, M2 Building
Date&Time: 09:45-12:00 on Thursdays
Objectives: This course aims to teach the fundamentals of machine learning (ML) and its applications in Electrical and Electronic Engineering.
Textbook:
- B. Boehmke and B. Greenwell, Hands-on Machine Learning with R, 1st Ed., CRC Press, 2020.
Reference Books:
- G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning with Applications in R, 2nd Ed., Springer, 2023.
- G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning with Applications in Python, 1st Ed., Springer, 2023.
- E. Alpaydın, Introduction to Machine Learning, 4th Ed., The MIT Press, 2020.
- A. Geron, Hands-on Machine Learning with Scikit-Learn, Keras & Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, 3rd Ed., O'Reilly, 2023.
- N. Matloff, The Art of Machine Learning: A Hands-on Guide to Machine Learning with R, 1st Ed., No Starch Press, 2024.
Contents:
- Course Introduction and Scope
- Fundamentals
  - Introduction to ML
    - Supervised Learning
      - Regression
      - Classification
    - Unsupervised Learning
  - Modelling Process
  - Feature and Target Engineering
- Supervised Learning
- Dimensionality Reduction
  - Principal Component Analysis (O*** Y*********)
  - Autoencoders (E****** E********)
- Clustering
  - K-Means Clustering (O*** H*****)

Exams