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.

Instructor: Prof. Example

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.