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.