Last modified: 31 Jul 2023 11:19
This course will deliver the most sophisticated Machine Learning methodologies and algorithms which would be illustrated across a wide range of applications including but not limited to images, videos, health, time series data, language processing, etc. This course provides students with the Machine Learning principles for continuing learning and working in the area of Data Science and Artificial Intelligence.
|Session||First Sub Session||Credit Points||15 credits (7.5 ECTS credits)|
This course will present advanced Machine Learning principles with applications. Practical will help students to understand principles applying to real-world applications using cutting-edge tools and libraries. The lectures will cover supervised learning, unsupervised learning, and as well as reinforcement learning. This will include regression and classification models, stochastic gradient descent algorithms, automatic differentiation, deep neural networks, model assessment and selection, model evaluation, generative adversarial networks, reinforcement learning, unsupervised learning models.
Information on contact teaching time is available from the course guide.
2x Project & Implementation of Software/Program (50% each) (100% in total)
Resubmission of failed elements (pass marks carried forward).
There are no assessments for this course.
|Knowledge Level||Thinking Skill||Outcome|
|Factual||Remember||ILO’s for this course are available in the course guide.|