0fe3c84636
Implements the course schedule feature requested in issue #[2258](https://github.com/alshedivat/al-folio/issues/2258). This PR adds a new course schedule feature to the al-folio theme, allowing academics to easily create and display structured course information. **Changes:** - Added a `courses` collection to organize and display academic courses - Created course layout and display templates with responsive design - Implemented organization by year and term with automatic sorting - Added support for weekly schedule with topics and course materials - Simplified documentation with a README for course creation This feature makes it easier for academics to showcase their teaching materials with a consistent, organized display of course schedules, helping users create professional teaching pages without custom implementation. --------- Signed-off-by: George Araújo <george.gcac@gmail.com> Co-authored-by: George Araújo <george.gcac@gmail.com>
114 lines
3.3 KiB
Markdown
114 lines
3.3 KiB
Markdown
---
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layout: course
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title: Introduction to Machine Learning
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description: 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.
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instructor: Prof. Example
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year: 2023
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term: Fall
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location: Main Campus, Room 301
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time: Tuesdays and Thursdays, 10:00-11:30 AM
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course_id: intro-machine-learning
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schedule:
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- week: 1
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date: Sept 5
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topic: Course Introduction
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description: Overview of machine learning, course structure, and expectations.
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materials:
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- name: Syllabus
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url: /assets/pdf/example_pdf.pdf
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- name: Slides
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url: /assets/pdf/example_pdf.pdf
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- week: 2
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date: Sept 12
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topic: Linear Regression
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description: Introduction to linear regression, gradient descent, and model evaluation.
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materials:
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- name: Lecture Notes
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url: /assets/pdf/example_pdf.pdf
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- name: Assignment 1
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url: /assets/pdf/example_pdf.pdf
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- week: 3
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date: Sept 19
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topic: Classification
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description: Logistic regression, decision boundaries, and multi-class classification.
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materials:
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- name: Lecture Notes
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url: /assets/pdf/example_pdf.pdf
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- name: Coding Lab
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url: https://github.com/
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- week: 4
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date: Sept 26
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topic: Decision Trees and Random Forests
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description: Tree-based methods, ensemble learning, and feature importance.
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materials:
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- name: Lecture Notes
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url: /assets/pdf/example_pdf.pdf
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- name: Assignment 2
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url: /assets/pdf/example_pdf.pdf
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- week: 5
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date: Oct 3
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topic: Support Vector Machines
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description: Margin maximization, kernel methods, and support vectors.
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materials:
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- name: Lecture Notes
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url: /assets/pdf/example_pdf.pdf
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- name: Review Materials
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url: /assets/pdf/example_pdf.pdf
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- week: 6
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date: Oct 10
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topic: Midterm Exam
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description: Covers weeks 1-5.
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- week: 7
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date: Oct 17
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topic: Neural Networks Fundamentals
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description: Perceptrons, multilayer networks, and backpropagation.
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materials:
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- name: Lecture Notes
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url: /assets/pdf/example_pdf.pdf
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- name: Assignment 3
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url: /assets/pdf/example_pdf.pdf
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- week: 8
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date: Oct 24
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topic: Deep Learning
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description: Convolutional neural networks, recurrent neural networks, and applications.
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materials:
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- name: Lecture Notes
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url: /assets/pdf/example_pdf.pdf
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- name: Coding Lab
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url: https://github.com/
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---
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## Course Overview
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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:
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- Understand key machine learning paradigms and concepts
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- Implement basic machine learning algorithms
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- Evaluate and compare model performance
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- Apply machine learning techniques to real-world problems
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## Prerequisites
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- Basic knowledge of linear algebra and calculus
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- Programming experience in Python
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- Probability and statistics fundamentals
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## Textbooks
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- Primary: "Machine Learning: A Probabilistic Perspective" by Kevin Murphy
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- Reference: "Pattern Recognition and Machine Learning" by Christopher Bishop
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## Grading
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- Assignments: 40%
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- Midterm Exam: 20%
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- Final Project: 30%
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- Participation: 10%
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