AI and Machine Learning
| Code | School | Level | Credits | Semesters |
| DATA2006 | Computer Science | 2 | N/A | Full Year UK |
- Code
- DATA2006
- School
- Computer Science
- Level
- 2
- Credits
- N/A
- Semesters
- Full Year UK
Summary
This module gives brief introductions to a range of Artificial Intelligence and Machine Learning techniques. The module will cover the history of AI and techniques such as local search techniques, evolutionary algorithms, neural networks and deep learning.
It will prepare learners for further independent work on selecting appropriate techniques and developing their understanding and application of AI and ML techniques in practice.
Target Students
Only available to those studying towards the Data Scientist Degree apprenticeship programme
Classes
16 hours of distance learning video content and independent lab sheets supported by ad-hoc remote drop in sessions 4 hour block release day 20 hours total
Assessment
- 100% Assessment: Completion of the module
Assessed by end of designated period
Educational Aims
To give an understanding of contemporary artificial intelligence (AI) and machine learning (ML) methods.To develop skills in the practical implementation of AI and ML methods.To learn how to choose which methods to apply to a specific problem, and how the methods can be applied to solve practical problems in data science.To enable apprentices to apply data mining techniques on real data sets, some of which can be described as big data sets. Apprentices will also learn to appreciate both the potential and limitations of big data.Learning Outcomes
Understand key concepts of artificial intelligence and machine learning.
Be able to apply a range of artificial intelligence and machine learning techniques to data science problems.
Independently and systematically design, develop and test software as part of non-trivial data science problem.
Be able to implement AI techniques using languages and systems used in the contemporary workplace.
KSBs
K3. How data can be used systematically, through an awareness of key platforms for data and analysis in an organisation, including:
- Data processing and storage, including on-premise and cloud technologies.
- Database systems including relational, data warehousing & online analytical processing, “NoSQL” and real-time approaches; the pros and cons of each approach.
- Data-driven decision making and the good use of evidence and analytics in making choices and decisions.
K4. How to design, implement and optimise analytical algorithms – as prototypes and at production scale– using:
- Statistical and mathematical models and methods.
- Advanced and predictive analytics, machine learning and artificial intelligence techniques, simulations, optimisation, and automation.
- Applications such as computer vision and Natural Language Processing.
- An awareness of the computing and organisational resource constraints and trade-offs involved in selecting models, algorithms and tools.
- Development standards, including programming practice, testing, source control.
K5. The data landscape: how to critically analyse, interpret and evaluate complex information from diverse datasets:
- Sources of data including but not exclusive to les, operational systems, databases, web services, open data, government data, news and social media.
- Data formats, structures and data delivery methods including “unstructured” data.
- Common patterns in real-world data.
S1. Identify and clarify problems an organisation faces, and reformulate them into Data Science problems. Devise solutions and make decisions in context by seeking feedback from stakeholders. Apply scientific methods through experiment design, measurement, hypothesis testing and delivery of results. Collaborate with colleagues to gather requirements.
S2. Perform data engineering: create and handle datasets for analysis. Use tools and techniques to source, access, explore, prole, pipeline, combine, transform and store data, and apply governance (quality control, security, privacy) to data.
S3. Identify and use an appropriate range of programming languages and tools for data manipulation, analysis, visualisation, and system integration. Select appropriate data structures and algorithms for the problem. Develop reproducible analysis and robust code, working in accordance with software development standards, including security, accessibility, code quality and version control.
S4. Use analysis and models to inform and improve organisational outcomes, building models and validating results with statistical testing: perform statistical analysis, correlation vs causation, feature selection and engineering, machine learning, optimisation, and simulations, using the appropriate techniques for the problem.
S5. Implement data solutions, using relevant software engineering architectures and design patterns. Evaluate Cloud vs. on-premise deployment. Determine the implicit and explicit value of data. Assess value for money and Return on Investment. Scale a system up/out. Evaluate emerging trends and new approaches. Compare the pros and cons of software applications and techniques.
S8. Use project delivery techniques and tools appropriate to their Data Science project and organisation. Plan, organise and manage resources to successfully run a small Data Science project, achieve organisational goals and enable effective change.
B1. An inquisitive approach: the curiosity to explore new questions, opportunities, data, and techniques; tenacity to improve methods and maximise insights; and relentless creativity in their approach to solutions.
B2. Empathy and positive engagement to enable working and collaborating in multi-disciplinary teams, championing and highlighting ethics and diversity in data work.
B3. Adaptability and dynamism when responding to varied tasks and organisational timescales, and pragmatism in the face of real-world scenarios.
B5. An impartial, scientific, hypothesis-driven approach to work, rigorous data analysis methods, and integrity in presenting data and conclusions in a truthful and appropriate manner.
Conveners
- Adam Walker