Introduction to Machine Learning

Overview of Machine Learning

Definitions and Significance*:

Students will explore the fundamental concepts and various definitions of machine learning, understanding its crucial role in leveraging big data in numerous industries such as finance, healthcare, and more.

Types of Machine Learning:

The course will differentiate between the three main types of machine learning: supervised learning (where the model is trained on labeled data), unsupervised learning (where the model finds patterns in unlabeled data), and reinforcement learning (where an agent learns to behave in an environment by performing actions and receiving rewards).

Supervised Learning Algorithms

Regression Algorithms

Linear Regression*: Focuses on predicting a continuous variable using a linear relationship formed from the input variables. Polynomial Regression: Extends linear regression to model non-linear relationships between the independent and dependent variables. Decision Tree Regression: Uses decision trees to model the regression, helpful in capturing non-linear patterns with a tree structure.

Classification Algorithms

Logistic Regression: Used for binary classification tasks; extends to multiclass classification under certain methods like one-vs-rest (OvR). K-Nearest Neighbors (KNN): A non-parametric method used for classification and regression; in classification, the output is a class membership. Support Vector Machines (SVM): Effective in high-dimensional spaces and ideal for complex datasets with clear margin of separation. Decision Trees and Random Forest*: Decision Trees are a non-linear predictive model, and Random Forest is an ensemble method of Decision Trees. Naive Bayes: Based on Bayes’ Theorem, it assumes independence between predictors and is particularly suited for large datasets.

Ensemble Methods and Handling Imbalanced Data

Ensemble Techniques - Detailed techniques such as Bagging (Bootstrap Aggregating), Boosting, AdaBoost (an adaptive boosting method), and Gradient Boosting will be covered, emphasizing how they reduce variance and bias, and improve predictions. Strategies for Imbalanced Data* - Techniques such as Oversampling, Undersampling, and Synthetic Minority Over-sampling Technique (SMOTE) are discussed to handle imbalanced datasets effectively, ensuring that the minority class in a dataset is well-represented and not overlooked.

Unsupervised Learning Algorithms

Clustering Techniques

K-Means Clustering: A method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters. Hierarchical Clustering*: Builds a tree of clusters and is particularly useful for hierarchical data, such as taxonomies. DBSCAN: Density-Based Spatial Clustering of Applications with Noise finds core samples of high density and expands clusters from them. Association Rule Learning - Apriori and Eclat algorithms*: Techniques for mining frequent itemsets and learning association rules. Commonly used in market basket analysis.

Model Evaluation and Hyperparameter Tuning

Evaluation Metrics* - Comprehensive exploration of metrics such as Accuracy, Precision, Recall, F1 Score, and ROC-AUC for classification; and MSE, RMSE, and MAE for regression. Hyperparameter Tuning - Techniques such as Grid Search, Random Search, and Bayesian Optimization with tools like Optuna are explained. These methods help in finding the most optimal parameters for machine learning models to improve performance.