Applied Statistics and Machine Learning
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Learning Outcomes:
On completion of this module, learners will be able to:
1. Understand the key concepts and techniques for pattern recognition on complex data sets..
2. Decide when machine learning is an appropriate method to solve a problem.
3. Understand and apply machine learning algorithms such as linear regression, SVM, kNN, RF, DT etc.
4. Apply Machine Learning frameworks (e.g. scikit-learn, keras, tensorflow,…) to solve real-world problems.
5. Understand and design approaches to process data (voice, image etc.) and extract certain patterns of interest from large datasets.
Learning Activities:
Each week will consist of a number of different activities:
1. Introduction to concepts and theory using slides/ OneNote recorded in Panopto and live zoom lecturing.
2. Implementation of theory using hands on examples in Python
3. Q and A
4. Tutorials
Overview of Assessment:
CA1
Assessment Title & Description :
Task :
MIMLOs being assessed :
Individual/Group :
CA 1
Real world data processing and pattern recognition
1,4,5
Group
CA2
Assessment Title & Description :
Task :
MIMLOs being assessed :
Individual/Group :
CA 2
AI –based technology project using ML with Python
2,3,4,5
Individual
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