Discrete Mathematics II
This lecture covers fundamental notions and results in discrete and computational geometry.
Term: Spring
Time: Tuesdays and Thursdays, 10:00-11:30 AM
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
- Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars - Computational Geometry
- Jiří Matoušek - Lectures on Discrete Geometry
Schedule
| Week | Date | Topic | Materials |
|---|---|---|---|
| 1 | Sept 5 | Course Introduction Overview of machine learning, course structure, and expectations. | |
| 2 | Sept 12 | Linear Regression Introduction to linear regression, gradient descent, and model evaluation. | |
| 3 | Sept 19 | Classification Logistic regression, decision boundaries, and multi-class classification. | |
| 4 | Sept 26 | Decision Trees and Random Forests Tree-based methods, ensemble learning, and feature importance. | |
| 5 | Oct 3 | Support Vector Machines Margin maximization, kernel methods, and support vectors. | |
| 6 | Oct 10 | Midterm Exam Covers weeks 1-5. | |
| 7 | Oct 17 | Neural Networks Fundamentals Perceptrons, multilayer networks, and backpropagation. | |
| 8 | Oct 24 | Deep Learning Convolutional neural networks, recurrent neural networks, and applications. |