F25 Introduction to Architectural Robotics

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.

Instructor: Joshua Bard, Jiaying Wei (TA/CO-INSTRUCT)

Term: Fall

Location: Main Campus, Room 301

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

  • 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 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.