Teaching Responsibility

LJMU Schools involved in Delivery:

Computer Science and Mathematics

Learning Methods

Lecture

Practical

Module Offerings

7504BDSA-JAN-PAR

Aims

The course provides students with a detailed knowledge about artificial learning systems, covering both supervised and unsupervised learning. The course considers various machine learning techniques: Regression and Statistical Models, Classification, Clustering, Decision Trees, Support Vector Machines, Boosting, Neural Networks, Bayesian Networks, Computational Methods, and Simulation Techniques.

Learning Outcomes

1.
Develop theoretical models for machine learning systems.
2.
Design practical systems related to machine learning.
3.
Effectively implement various machine learning tools.
4.
Conduct research in machine learning.
5.
Critically evaluate various machine learning approaches.

Module Content

Outline Syllabus:Introduction to machine learning systems Neural networks Regression Decision trees, Naïve Bayes and KNN (classification), K-means clustering Support vector machine Reinforcement Learning Evolutionary computing in machine learning Particle Swarm Intelligence techniques Hybrid machine Learning systems Machine Learning and cloud
Additional Information:The module contributes to the master’s aim to equip the student with the required abilities and skills to perform data science on real-world applications.

Assessments

Report

Presentation

Exam