Machine Learning for Business

BMAN60422 Machine Learning for Business 15 Semester 2 Postgraduate Taught Level 7 Julia Handl Unit coordinator Yu-Wang Chen Unit coordinator Alliance Manchester Business School 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 This course covers the fundamentals of machine learning as a core area of artificial intelligence, introduces key machine learning techniques across three main categories: supervised learning, unsupervised learning, and reinforcement learning, and explores the challenges and solutions of applying machine learning to solve real-world business problems. This course covers the fundamentals of machine learning as a core area of artificial intelligence, introduces key machine learning techniques across three main categories: supervised learning, unsupervised learning, and reinforcement learning, and explores the challenges and solutions of applying machine learning to solve real-world business problems. <ul><li>Provide students with an understanding of learning as a core area of artificial intelligence and its applications in business.</li><li>Introduce key machine learning techniques across three main categories: supervised learning, unsupervised learning, and reinforcement learning.</li><li>Explore the challenges and solutions of applying machine learning to solve real-world business problems, with an emphasis on model interpretability, data quality issues, and imbalanced datasets. </li></ul> At the end of the course unit, student should be able to:<ul> <li>Understand the fundamentals of data analytics and its application to business and management decision making,</li> <li>Understand a variety of data analysis techniques, such as data preprocessing, classification and clustering, prediction and forecasting, association rule mining & text mining, etc.,</li> <li>Discuss how visual analytics can be used to understand big data, extract insights and identify patterns,</li> <li>Demonstrate the ability to use specialised software tools to analyse large sets of data in real-world problems. </li></ul> <ul><li>Understand the fundamentals of machine learning in the context of artificial intelligence</li><li>Understand and critically evaluate key machine learning techniques across the three main categories: supervised learning, unsupervised learning, and reinforcement learning.</li><li>Understand the importance of interpretable machine learning for business applications.</li></ul> <ul><li>Frame real-world business problems through a machine learning lens and identify the appropriate modelling paradigm for a given problem.</li></ul> <ul><li>Demonstrate the ability to use specialised software tools and programming packages to analyse large datasets and build machine learning models for real-world business problems.</li></ul> <ul><li>Develop digital and teamwork skills through case studies and coursework project.</li></ul> <ul><li>Introduction to Machine Learning: providing an overview of the fundamental concepts and definitions of machine learning in the context of artificial intelligence.</li><li>Supervised Learning: covering the basics from descriptive to predictive analytics and introducing various predictive modelling techniques, such as regressions, decision tree models, and neural networks.</li><li>Unsupervised Learning: exploring clustering analysis, association analysis, and collaborative filtering.</li><li>Reinforcement Learning: studying reinforcement learning and sequential decision-making in a stochastic environment.</li><li>Ensemble Learning: exploring bootstrap aggregating, boosting, stacking, and XGBoost.</li><li>Interpretability in Machine Learning: discussing the importance of interpretable machine learning for business applications.</li><li>Machine Learning for Business: challenges and solutions: addressing key challenges in machine learning for business, such as data quality issues and imbalanced datasets, while exploring their potential solutions and future advancements. </li></ul> Formal Contact MethodsMinimum Contact hours: 20 Delivery format: Lecture and Workshops  0 Other 100% 50% Exam50% Group Coursework <ul><li>Generic feedback posted on Blackboard regarding overall examination performance. </li><li>Written and/or verbal comments on assessed or non-assessed coursework. </li><li>Responses to student emails and questions from a member of staff including feedback provided to a group via an online discussion forum. </li></ul> BMAN60422 Programme Req: BMAN60422 is only available as a core unit to students on MSc Business Analytics and as an elective to students on MEng (Hons) Computer ScienceBMAN60422 is available as a core unit to students on MSc Business Analytics and as an elective with permission to students on other relevant MSc programmes.  N Galit Shmueli,et al.; Data Mining for Business Analytics: Concepts, Techniques, and Applications - in R (e-book available from the university library) or in Python, John Wiley & Sons, 2018. INFORMS Analytics Magazine, http://www.analytics-magazine.org/   Lectures 20 Practical classes & workshops 10 0 120 Informal Contact Method<ul> <li> Office Hours</li> <li> Online Learning Activities (blogs, discussions, self assessment questions)</li></ul>