Maching Learning
This course provides a comprehensive introduction to Machine Learning, covering both theoretical foundations and practical applications. It introduces students to supervised and unsupervised learning, focusing on model evaluation, feature selection, clustering, decision trees, and regression techniques.
Students will develop hands-on expertise in building, tuning, and evaluating models while understanding the mathematical principles behind them. The course emphasizes best practices in data preprocessing, model selection, and performance optimization, equipping students with the necessary tools to apply Machine Learning in real-world scenarios across various industries.
Linear & Logistic Regression
- Simple vs. Multiple Linear Regression
- Assumptions (Gauss-Markov Theorem)
- Model Inference & Statistical Testing
- t-tests
- F-tests
- Confidence Intervals
- Handling Categorical Variables
- Dummy Encoding
- Polynomial Regression
- Logistic Regression for Classification
- Practical Example: Predicting house prices with multiple regression
Decision Trees
- Tree-Based Model Structure
- Splitting Criteria
- Gini Index
- Entropy
- Overfitting Prevention
- Pre-Pruning
- Post-Pruning
- Computational Complexity Considerations
- Practical Example: Decision tree classification on real-world data
Model Evaluation & Feature Selection
- Understanding Bias-Variance Trade-off
- Cross-Validation Techniques
- Evaluation Metrics
- MAE
- RMSE
- R²
- AUC-ROC
- Confusion Matrix
- Feature Selection Techniques
- Wrapper
- Filter
- Regularization
- Hyperparameter Tuning
- Grid Search
- Bayesian Optimization
- AutoML
- Practical Example: Model selection and tuning using Python
Clustering Techniques
- Introduction to Unsupervised Learning
- Hierarchical Clustering
- Agglomerative
- Divisive
- Partitioning Methods
- K-Means
- K-Medoids
- Density-Based Clustering
- DBSCAN
- OPTICS
- Gaussian Mixture Models
- Expectation-Maximization
- Practical Example: Clustering customer data for segmentation