This course introduces students to key concepts and techniques in data mining, focusing on data wrangling, clustering, density-based clustering, Gaussian Mixture Models, dimensionality reduction, kernel methods, and Support Vector Machines (SVM). It also covers model evaluation and tuning. The course is designed for students with minimal background in university-level mathematics or computer science.
1. Data wrangling (1.1 Overview of data mining , 1.2 Data cleaning techniques. , 1.3 Handling missing data and outliers. , 1.4 Merging and joining datasets. , 1.5 Data transformation and feature engineering. , 1.6 Data normalisation and scaling.)
2. Clustering Algorithms (2.1 Introduction to clustering. , 2.2 K-means clustering algorithm. , 2.3 Hierarchical clustering. , 2.4 Agglomerative and divisive approaches. , 2.5 Dendrograms and their interpetation. , 2.6 Introduction to density-based clustering. , 2.7 DBSCAN Density-Based Spatial Clustering of Applications with Noise. , 2.8 Identifying clusters of arbitrary shape and noise points. , 2.9 Introduction to GMMs, 2.10 Expectation-Maximisation algorithm. , 2.11 Applications of GMM in clustering.)
3. Dimensionality Reduction (3.1 Importance of dimensionality reduction in data mining. , 3.2 Principal Component Analysis PCA. , 3.3 Applying Singular Value Decomposition SVD.)
4. Kernel Methods (4.1 Introduction to kernel methods. , 4.2 Kernel trick and its applications. , 4.3 Support Vector Machines SVM with kernel methods.)
5. Model Evaluation (5.1 Evaluation metrics: accuracy, precision, recall, F1 score. , 5.2 Cross-validation techniques. , 5.3 Hyperparameter tuning using grid search.)
6. Project (6.1 Integration of all course components in a final project. , 6.2 Presentation and group-work skills for data mining projects.)
By the end of the course, students should be able to do the following:
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SCQF Level: 11
Credits: 15