Introduction to Machine Learning
This course provides an introduction to machine learning concepts, algorithms, and applications. Students will learn about supervised and unsupervised learning, model evaluation, and practical implementations.
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%
授業計画
- 第回 Course Introduction Sept 5
Overview of machine learning, course structure, and expectations.
- 第回 Linear Regression Sept 12
Introduction to linear regression, gradient descent, and model evaluation.
- 第回 Classification Sept 19
Logistic regression, decision boundaries, and multi-class classification.
- 第回 Decision Trees and Random Forests Sept 26
Tree-based methods, ensemble learning, and feature importance.
- 第回 Support Vector Machines Oct 3
Margin maximization, kernel methods, and support vectors.
- 第回 Midterm Exam Oct 10
Covers weeks 1-5.
- 第回 Neural Networks Fundamentals Oct 17
Perceptrons, multilayer networks, and backpropagation.
- 第回 Deep Learning Oct 24
Convolutional neural networks, recurrent neural networks, and applications.