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Jiahao Zhang 0fe3c84636
Add course schedule feature to teaching page (#2258) (#3147) 
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>
2026-01-17 18:43:47 -03:00

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layout title description instructor year term location time course_id schedule
course 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. Prof. Example 2023 Fall Main Campus, Room 301 Tuesdays and Thursdays, 10:00-11:30 AM intro-machine-learning
week date topic description materials
1 Sept 5 Course Introduction Overview of machine learning, course structure, and expectations.
name url
Syllabus /assets/pdf/example_pdf.pdf
name url
Slides /assets/pdf/example_pdf.pdf
week date topic description materials
2 Sept 12 Linear Regression Introduction to linear regression, gradient descent, and model evaluation.
name url
Lecture Notes /assets/pdf/example_pdf.pdf
name url
Assignment 1 /assets/pdf/example_pdf.pdf
week date topic description materials
3 Sept 19 Classification Logistic regression, decision boundaries, and multi-class classification.
name url
Lecture Notes /assets/pdf/example_pdf.pdf
name url
Coding Lab https://github.com/
week date topic description materials
4 Sept 26 Decision Trees and Random Forests Tree-based methods, ensemble learning, and feature importance.
name url
Lecture Notes /assets/pdf/example_pdf.pdf
name url
Assignment 2 /assets/pdf/example_pdf.pdf
week date topic description materials
5 Oct 3 Support Vector Machines Margin maximization, kernel methods, and support vectors.
name url
Lecture Notes /assets/pdf/example_pdf.pdf
name url
Review Materials /assets/pdf/example_pdf.pdf
week date topic description
6 Oct 10 Midterm Exam Covers weeks 1-5.
week date topic description materials
7 Oct 17 Neural Networks Fundamentals Perceptrons, multilayer networks, and backpropagation.
name url
Lecture Notes /assets/pdf/example_pdf.pdf
name url
Assignment 3 /assets/pdf/example_pdf.pdf
week date topic description materials
8 Oct 24 Deep Learning Convolutional neural networks, recurrent neural networks, and applications.
name url
Lecture Notes /assets/pdf/example_pdf.pdf
name url
Coding Lab https://github.com/

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%