Teaching Responsibility
LJMU Schools involved in Delivery:
Computer Science and Mathematics
Learning Methods
Lecture
Practical
Tutorial
Module Offerings
7145COMP-JAN-CTY
Aims
To develop knowledge of accelerated machine learning at masters level and provide guidance on the purpose, design and development of accelerated machine learning projects.
To provide an understanding of how the range of tools, techniques and algorithms can be applied for accelerated machine learning.
To provide help on establishing accelerated machine learning design and development principles to successfully complete large scale machine learning projects.
Learning Outcomes
1.
Describe in depth and detail the theoretical principles and objectives of accelerated Machine Learning (ML) using the Python-based NVIDIA RAPIDS framework
2.
Demonstrate deep understanding of relevant RAPIDS ML concepts and techniques
3.
Critically select appropriate RAPIDS ML algorithms to solve particular tasks
4.
Evaluate RAPIDS ML algorithms to determine their strengths and weaknesses
5.
Implement and test different RAPIDS ML algorithms using a suitable language, e.g. Python
6.
Evaluate the suitability of different processing architectures for specific computational tasks (CPU/GPU)
Module Content
Outline Syllabus:1. GPU Computing
2. Introduction to CUDA
3. cuDF Analytics
4. cuML Machine Learning
5. cuGraph Graph Analytics
6. Deep Learning
7. Visualisation
8. Apache Arrow
9. Accelerated Data Science
10. Applications in Accelerated Machine Learning
11. Performance, Validation and Model Interpretation; Future Large Scale Machine Learning
Module Overview:
This module provides the key skills required in accelerated machine learning to solve large scale machine learning problems. These skills will help to equip you with the fundamental principles of accelerated machine learning to support your final degree project. Furthermore, they will be practical core requirements for a successful career as a machine learning engineer in industry.
This module provides the key skills required in accelerated machine learning to solve large scale machine learning problems. These skills will help to equip you with the fundamental principles of accelerated machine learning to support your final degree project. Furthermore, they will be practical core requirements for a successful career as a machine learning engineer in industry.
Additional Information:This module provides the key skills required in accelerated machine learning to solve large scale machine learning problems. These skills will help to equip the student with the fundamental principles of accelerated machine learning to support the final degree project. Furthermore, these skills will be practical core requirements for a successful career as a machine learning engineer in industry.