Unsupervised Learning in AI/ML

Overview of unsupervised learning principles and its applications. Understanding the differences between supervised and unsupervised learning. Introduction to key concepts such as clustering, dimensionality reduction, and association rules.

Introduction to clustering as an unsupervised learning method. Discussion of various clustering techniques and their applications in data analysis.

Detailed exploration of the k-Means clustering algorithm. Understanding centroids, distance metrics, and convergence criteria. Hands-on implementation and evaluation of k-Means clustering on real datasets.

Examination of Hierarchical Clustering methods, including agglomerative and divisive approaches. Understanding dendrograms and linkage criteria. Practical exercises to apply hierarchical clustering.

Introduction to DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Explanation of density-based clustering and its advantages over other methods. Hands-on exercises for applying DBSCAN.

Introduction to dimensionality reduction techniques and their importance. Overview of methods to reduce the number of features while preserving important information.

Detailed study of PCA, including its mathematical foundation and applications. Practical exercises for implementing PCA to reduce dimensionality and visualize high-dimensional data.

Introduction to t-SNE for visualizing high-dimensional data in lower dimensions. Understanding its application in exploratory data analysis and clustering.

Overview of association rules and their use in discovering relationships between variables in large datasets. Introduction to key metrics such as support, confidence, and lift.

Detailed exploration of the Apriori algorithm for mining frequent itemsets and association rules. Hands-on implementation and analysis of results.

Application of association rules to market basket analysis. Understanding customer purchase patterns and how to derive actionable insights from transaction data.

Introduction to anomaly detection techniques for identifying unusual patterns in data. Discussion of various methods and their applications.

Detailed study of Isolation Forests for anomaly detection. Understanding the algorithm's approach to isolating anomalies and its practical applications.

Introduction to Gaussian Mixture Models for detecting anomalies. Understanding the concept of mixture models and their application in identifying outliers.

Apply the techniques learned in the course to a real-world dataset. Projects will involve clustering, dimensionality reduction, association rules, and anomaly detection. Students will analyze data, implement models, and interpret results.

Recap of key concepts and techniques covered in the course. Assessment through a written test and practical exercises. Feedback session to discuss performance and provide guidance on further learning.