The Professional Certificate in Machine Learning and Artificial Intelligence is an opportunity to acquire advanced technical expertise in machine learning and AI, along with the business acumen to put your knowledge into practice.
This 25-week online programme, developed jointly with the Department of Computing, will provide you with in-depth knowledge of fundamental and advanced concepts and trends in AI, the ability to determine when machine learning is feasible and can add value to business challenges, and the capacity to leverage the power of data and evaluate common machine learning methods to improve performance.
Throughout this programme, you will benefit from the deep knowledge of our expert faculty, learn from video lectures and hands-on activities, and receive personal support from programme advisors to help propel your career in digital marketing. At the end of the programme, you will complete a hands-on capstone project.
You will finish the programme prepared to implement what you have learned, a job-ready portfolio to showcase your projects, and with a verified Digital Certificate from Imperial College Business School Executive Education.
Who should attend?
This international programme is suitable for:
Early career IT and engineering professionals looking for hands on training in ML and AI
Data and Business Analytics professionals who wish to learn about the latest AI tools, techniques, and applications
Recent science, technology, engineering, and mathematics graduates and academics who are interested in moving into the AI and ML field
This programme will enable you to:
Understand fundamental and advanced concepts and trends in artificial intelligence, and its potential real-world implications
Determine when machine learning is feasible and can be applied to meaningfully address specific business challenges
Leverage the power of data and evaluate common machine learning methods to improve predictive performance and refine decision-making strategies
Develop and refine machine learning models using Python and industry-standard tools to measure and improve performance
Identify real-world problems and devise innovative solutions using machine learning models
What you will learn
Section I - Foundations of Machine Learning and Artificial Intelligence
- Module 1: Programme Orientation
- Module 2: Introduction to Machine Learning
- Module 3: Probability for Machine Learning
- Module 4: Statistics for Machine Learning
- Module 5: Generalisation Theory and the Bias-Variance Trade-Off
- Module 6: Evaluating Predictive Performance
- Module 7: Advanced Topics in Performance Evaluation
Section II - Methods for Learning from Data
- Module 8: Nearest Neighbour Methods
- Module 9: Decision Trees, Part I
- Module 10: Decision Trees, Part II
- Module 11: Naive Bayes
- Module 12: Bayesian Optimisation
- Module 13: Logistic Regression
- Module 14: Support Vector Machines
- Module 15: Unsupervised Learning
- Module 16: Principal Component Analysis
Section III - Advanced Topics in Artificial Intelligence and Machine Learning
- Module 17: Introduction to Deep Learning
- Module 18: Neural Networks
- Module 19: Hyperparameters
- Module 20: Transparency and Interpretability
- Module 21: Convolutional Neural Networks
- Module 22: Biological Basis for Convolutional Neural Networks
- Module 23: Reinforcement Learning
- Module 24: Hyperparameter Tuning
- Module 25: Capstone Competition
Professor Chris Tucci received the degrees of Ph.D. in Management from the Sloan School of Management, MIT; SM (Technology & Policy) from MIT; and BS (Mathematical Sciences), AB (Music), and MS (Computer Science) from Stanford University. He was an industrial computer scientist involved in developing Internet protocols and applying artificial intelligence tools.
Chris' primary area of interest is in how firms make transitions to new business models, technologies, and organisational forms. He also studies crowdsourcing, Internetworking, and digital innovations.
Professor Wolfram Wiesemann
Professor of Analytics & Operations
Professor Wiesemann is Professor of Analytics & Operations at Imperial College Business School, where he also serves as the Academic Director of the MSc Business Analytics programme. His teaching focuses on linear, discrete, and nonlinear optimisation and the theory, algorithms and applications of machine learning. He also teaches prescriptive analytics.
Wolfram holds a joint master's degree in management and computing from Darmstadt University of Technology, and a PhD in operations research from Imperial College London. He is also a Fellow of the KPMG Centre for Advanced Business Analytics.
Professor, Computational Optimisation
Professor Ruth Misener is a Professor of Computational Optimisation in the Department of Computing at Imperial College London. Her research focuses on numerical optimisation algorithms and computational software frameworks, with current applications including bioprocess optimisation under uncertainty and petrochemical process network design and operations.
Ruth earned a BS in chemical engineering from the Massachusetts Institute of Technology and a PhD in chemical engineering from Princeton University. She is the recipient of numerous awards and fellowships and is currently an EPSRC Early Career Research Fellow at Imperial College London
Alex Ribeiro Castro
Data Scientist, Senior Teaching Fellow
Dr Alex Ribeiro-Castro is a Data Scientist and Senior Teaching Fellow with the Operations Management Department at Imperial College Business School. He also teaches on the Global Business Analytics MSc. In addition, he has more than 10 years of teaching experience in pure and applied mathematics at institutions across the globe. He has more than five years of consultancy experience, including machine learning and optimisation problems, in industries such as fintech, health, energy, and more recently in retail.
Alex holds an MA and a PhD in mathematics from the University of California (Santa Cruz) and a professorship in mathematics from the Pontifical Catholic University (PUC-Rio)
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