Machine Learning and Predictive Analytics - Professional course

Program Overview

This professional course in Machine Learning and Predictive Analytics equips students with the knowledge and skills to understand and apply these techniques in a business context. It covers topics such as predictive analytics, machine learning, and data visualization, and prepares graduates for careers in data science, machine learning, or business analytics across various sectors. The course is delivered through lectures, tutorials, and group work, and is assessed through a coursework project and a written exam.

Program Outline

Outline:

This professional course is designed to equip students with the knowledge and skills to understand and apply machine learning and predictive analytics in a business context. It covers the following topics:

Content:

• Introduction to predictive analytics
• Business relevance of predictive analytics
• Relevance of pattern recognition, classification, and optimization
• Predictive analytics and big data
• Case studies of business applications using predictive analytics approaches
• Sources of data and value of knowledge
• Applications for predictive analytics: marketing and recommender systems, fraud detection, business process analytics, credit risk modeling, web analytics, and others
• Social media and human behavior analytics
• Case study: email targeting
• Analytics models and techniques
• Types of analytics models: predictive, survival, and descriptive models
• Definition of pattern recognition, inferring data, and data visualization
• Brief introduction to learning and regression approaches
• Comparison of different approaches based on use and goals
• Introduction to machine learning
• Basic principles and notions of learning
• Introduction to learning problems (classification, clustering, and reinforcement) and relevant literature
• Identifying different learning approaches: supervised, unsupervised, and reinforcement
• Case study on different types of learning
• Machine learning for predictive analytics
• Review of types of problems
• Machine learning techniques: decision tree learning, artificial neural networks, clustering, Naive Bayes classifier, k-nearest neighbors, genetic algorithms
• Case study on choosing a suitable predictive modeling technique
• Regression techniques for predictive analytics
• Review of types of problems (application)
• Linear regression models
• Survival or duration analysis (time to event analysis)
• Ensemble learning and random forest
• Case study on choosing a suitable predictive modeling technique
• Advanced topics and software tools
• Analytics in the context of big data
• Predictive analytics as art and science
• Software tools: the R project and Python
• Trends and challenges in predictive analytics: future directions

Course Schedule:

• The course is delivered through weekly lectures and tutorial sessions, which take place on consecutive weeks.
• Each lecture introduces new ideas and skills.
• Small group tutorial sessions enable students to carry out study and research exercises under the guidance of a tutor.
• The teaching material is available from Blackboard (UWE's online learning environment).
• A course text is also recommended.

Assessment:

• The module will be assessed through a coursework project and a written exam.
• More details are available in the university's glossary of assessment terms.

Teaching:

• The module is delivered by experienced tutors with expertise in machine learning and predictive analytics.
• The teaching methods include lectures, tutorials, and group work.
• Students have access to a range of resources, including the university library and online learning environment.

Careers:

• This course can help students to develop the skills and knowledge needed for a career in data science, machine learning, or business analytics.
• Graduates can find employment in a variety of sectors, including finance, marketing, healthcare, and technology.

Other:

• The course is open to students with a first degree at 2.2 level or above (or equivalent), or six months of relevant industrial experience.
• Non-UK students will need to show their passport on entry to the UK.
• Students whose first language is not English will need to provide evidence that they meet the UK Border Agency and the university's minimum English language requirements.
• Students are encouraged to speak to the course tutor before enrolling if they are unsure about their suitability for the course.

UK students:

£792.00

International students:

£1,333.00

• Fees displayed are based on 2023/24 entry and are an indication only.