Machine Learning Curriculum

Course Description:

This course provides an introduction to the field of Machine Learning (ML). Students will learn the foundational concepts and techniques used in ML, including supervised learning, unsupervised learning, and deep learning.

Week 1: Introduction to Machine Learning

• Overview of Machine Learning
• History of ML
• Applications of ML

Week 2: Supervised Learning

• Introduction to supervised learning
• Linear regression
• Logistic regression
• Decision trees and random forests
• Support vector machines (SVMs)

Week 3: Unsupervised Learning

• Introduction to unsupervised learning
• Clustering (e.g., K-means, hierarchical clustering)
• Dimensionality reduction (e.g., PCA)
• Association rule learning

Week 4: Neural Networks

• Introduction to neural networks
• Feedforward neural networks
• Backpropagation
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)

Week 5: Deep Learning

• Introduction to deep learning
• Autoencoders
• Generative Adversarial Networks (GANs)
• Applications of deep learning

Week 6: Model Evaluation and Validation

• Cross-validation
• Bias-variance tradeoff
• Evaluation metrics (e.g., accuracy, precision, recall, F1 score)

Week 7: Advanced Topics

• Ensemble methods (e.g., random forests, boosting)
• Reinforcement learning
• Natural Language Processing (NLP)
• Time series analysis

Week 8: Final Project

• Students work on a project applying ML techniques to a real-world problem

Assessment:

• Weekly quizzes or assignments
• Final project demonstration and submission

Prerequisites:

Basic knowledge of programming (preferably Python) and mathematics (linear algebra, calculus, probability) is recommended. No prior knowledge of Machine Learning is required.