F21DL - Data Mining and Machine Learning
Course leader(s):
Aims
In this course, students will develop:
- An understanding of the fundamental concepts and techniques used in data mining and machine learning.
- An understanding of the mathematics underpinning data mining and machine learning.
- A critical awareness of the appropriateness of different data mining and machine learning techniques and the relationships between them.
- Familiarity with common applications of data mining and machine learning techniques.
Syllabus
1. Data sets (1.1 - Knowledge representation , 1.2 - Data Exploration and Visualization, 1.3 - Data preparation and feature selection)
2. Machine Learning workflow (2.1 - Machine learning building blocks , 2.2 - Model Testing and evaluation , 2.3 - Hyperparameter tuning, 2.4 - Basic classifiers)
3. Unsupervised learning (3.1 - K means clustering, 3.2 - Cluster evaluation , 3.3 - Cluster visualization)
4. Supervised Learning (4.1 - Generative vrs discrinminative models, 4.2 - Bayes Classifier bayes rule , bayes inference, 4.3 - Decision Trees building decision trees, splitting criteria)
5. Linear Classification (5.1 - Linear Regression, 5.2 - Logistic Regression, 5.3 - Gradient descent rule)
6. Neural networks (6.1 - Single layer Perceptron , 6.2 - Multi-layer perceptron, 6.3 - Convolutional Neural networks)
Learning outcomes
By the end of the course, students should be able to do the following:
- Construct key building blocks for data mining and machine learning including data preparation, feature extraction, model training and evaluation.
- Differentiate between some popular machine learning approaches related to supervised and unsupervised learning.
- Practice the mathematics underpinning methodologies of data mining and machine learning.
- Assess the benefits and limitations of using a variety of machine learning techniques for solving practical applications.
- Choose appropriate solutions for practical problems based on complex issues like data quality, model complexity and overfitting.
- Select a variety of software libraries to process data and evaluate machine learning pipelines.
- Conduct research independently to tackle practical problems.
Further details
Curriculum explorer: Click here
SCQF Level: 11
Credits: 15
