Introduction to Machine Learning
Introduction to the basic concepts, algorithms and applications of machine learning. Supervised and unsupervised learning methods, model evaluation and implementation in Python (Scikit-learn, TensorFlow/PyTorch).
Course Notes
Introduction What is Machine Learning? Types, Application Areas.
Data Preprocessing: Cleaning, Normalization, Feature Selection
Linear Regression: Simple and Multiple Regression, Cost Function.
Gradient Descent: Algorithm and Variations.
Logistic Regression: Classification Problems, Decision Boundaries.
Decision Trees: Entropy, Information Gain, Pruning
Random Forests and Ensemble Learning
Support Vector Machines (SVM): Basic Concepts, Kernel Functions.
Midterm Exam
Unsupervised Learning: Clustering - K-Means, Hierarchical Clustering.
Dimensionality Reduction: Principal Component Analysis (PCA)
Introduction to Artificial Neural Networks (ANN): Perceptron, Multilayer Neural Networks.
Training Neural Networks: Backpropagation Algorithms.
Model Evaluation and Selection: Cross-Validation, Metrics.
Project Presentations and Final Exam.