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
The post Applied Statistics and Machine Learning first appeared on Krita Infomatics.
The post Applied Statistics and Machine Learning appeared first on Krita Infomatics.