Introduction-to-Machine-Learning-Course

This repository contains materials for the Introduction to Machine Learning course (67577) for the Second Semester, 2024.

Course Structure

The course is divided into several exercises, each focusing on different aspects of machine learning. Each exercise is contained in its own directory with relevant files and documentation.

Exercise Directories

1. exercise1/: Linear Regression and Mathematical Background
2. exercise2/: Classification
3. exercise3/: PAC Learning, Regularization, Ensemble Methods & Cross Validation
4. exercise4/: Gradient-Based Learning

Exercise Details

Exercise 1: Linear Regression and Mathematical Background

• Files:
  • linear_regression.py
  • polynomial_fitting.py
  • house_price_prediction.py
  • city_temperature_prediction.py
• Topics:
  • Mathematical background (Linear Algebra, Multivariate Calculus)
  • Linear Regression
  • Polynomial Fitting

Exercise 2: Classification

• Files:
  • loss_functions.py
  • classifiers.py
  • classifiers_evaluation.py
• Topics:
  • Perceptron Classifier
  • Gaussian Naive Bayes
  • Linear Discriminant Analysis (LDA)

Exercise 3: PAC Learning, Regularization, Ensemble Methods & Cross Validation

• Files:
  • decision_stump.py
  • adaboost.py
  • adaboost_scenario.py
  • loss_functions.py
• Topics:
  • PAC Learnability
  • VC-Dimension
  • Agnostic-PAC
  • AdaBoost

Exercise 4: Gradient-Based Learning

• Files:
  • gradient_descent.py
  • learning_rate.py
  • modules.py
  • logistic_regression.py
  • gradient_descent_investigation.py
• Topics:
  • Convex Optimization
  • Gradient Descent
  • Regularized Logistic Regression