Basic and Advanced Machine Learning and Data Assimilation
TBC
Instructor: Prof. Mark Asch
Location: Caraga State University, Philippines.
Course Overview
This introductory course on machine learning covers fundamental concepts and algorithms in the field. By the end of this course, students will be able to:
- Understand key machine learning paradigms and concepts
- Implement basic machine learning algorithms
- Evaluate and compare model performance
- Apply machine learning techniques to real-world problems
Prerequisites
- Basic knowledge of linear algebra and calculus
- Programming experience in Python
- Probability and statistics fundamentals
Textbooks
- Primary: “Machine Learning: A Probabilistic Perspective” by Kevin Murphy
- Reference: “Pattern Recognition and Machine Learning” by Christopher Bishop
Grading
- Assignments: 40%
- Midterm Exam: 20%
- Final Project: 30%
- Participation: 10%
Schedule
| Week | Date | Topic | Materials |
|---|---|---|---|
| 1 | Course Introduction Overview of machine learning, course structure, and expectations. | ||
| 2 | Linear Regression Introduction to linear regression, gradient descent, and model evaluation. | ||
| 3 | Classification Logistic regression, decision boundaries, and multi-class classification. | ||
| 4 | Decision Trees and Random Forests Tree-based methods, ensemble learning, and feature importance. | ||
| 5 | Support Vector Machines Margin maximization, kernel methods, and support vectors. | ||
| 6 | Midterm Exam Covers weeks 1-5. | ||
| 7 | Neural Networks Fundamentals Perceptrons, multilayer networks, and backpropagation. | ||
| 8 | Deep Learning Convolutional neural networks, recurrent neural networks, and applications. |