Postgraduate Diploma in Machine Learning and Artificial Intelligence (E-Learning)

Who Is This Diploma Designed For?

With respect to the growth and demand in the artificial intelligence (AI) field, a whitepaper published by the Chinese tech giant Tencent’s research arm says there are just 300,000 AI researchers and practitioners worldwide, but the market demand is for millions of roles.

Reuters reports that many economists believe AI has the potential to change the economy’s basic trajectory in the same way that, say, electricity or the steam engine did. The following graphics represent a supply and demand comparison over the last half-decade.

Machine learning (ML), a subfield of AI, makes up the largest chunk of investment made in the AI field. A research report by Research and Markets predicts that the ML market will grow at a CAGR of 44.1 percent by 2022, taking the total investment to a staggering USD $8.81 billion.

Glassdoor estimates that average salaries for AI-related jobs advertised on company career sites, including jobs in machine learning, rose 11 percent between October 2017 and September 2018, to USD $123,069 annually.

These reports serve as credible sources about the demand for skills related to ML and AI. Even though currently you might not have directly relevant experience in AI, there is no denying that given the growth in the field of AI, you will come across opportunities to use your existing industry knowledge and the new skills acquired via our applied AI program.

We are unable to comment about the specific details of diplomas from other providers. We would encourage you to list the things you are looking for in a diplomas and then compare our diploma with other providers’ diplomas. We have listed some of the parameters our learners have found relevant:

Attributes EMERITUS Benefit
Type SPOC (Small Private Online Course) — typically cohorts of 150 people, with individualized attention
Delivery Fixed start and end dates
Duration Nine months
  • Dedicated support for academic and non-academic queries
  • Follow-up to nudge you along to completion
  • Discussion board to debate and learn with the cohort
  • Application projects/assignments
  • Discussions
  • Live classes
  • Video lectures
Grading All quizzes and assignments are graded
Learning Outcome
  • 1. Supervised learning techniques
    • Regression, Bayesian methods, foundational classification algorithms, refinements to classification techniques
    • Intermediate classification techniques such as support-vector machines, trees, forests, and boosting
  • 2. Unsupervised learning techniques
    • Clustering methods
    • Recommendation systems
    • Sequential data models such as Markov models
    • Association analysis and model selection
  • 3. Essentials of creating intelligent systems
  • 4. Foundation of AI
  • 5. Tools and techniques that make a system intelligent
    • Search techniques
    • Machine-learning algorithms to group data points or split datasets to find insights
    • Finding fast and optimal solutions to highly complex problems bound by real-world constraints
    • Decide the best logical course of action to achieve its goal via agents
    • Deep learning and neural networks
    • Applications of AI
Faculty John W. Paisley has a PhD from Duke and has been a postdoctoral researcher in the computer science departments at Princeton University and UC Berkeley. John Paisley’s research focuses on developing models for large-scale text and image processing applications. He is particularly interested in Bayesian models and posterior inference techniques that address the big data problem.

Prof. Ansaf Salleb-Aouissi received her PhD in Computer Science from the University of Orleans, France. She was an associate research scientist at the Columbia University’s Center for Computational Learning Systems and served as an adjunct professor with the Computer Science department and the Data Science Institute.

Ansaf’s research interests lie in machine learning and artificial intelligence. She has done research on frequent patterns mining, rule learning, and action recommendation, and has worked on projects including geographic information systems and machine learning for the power grid.

Diploma Fee USD $3,000
Credential Postgraduate diploma in ML & AI from EMERITUS in collaboration with Columbia Engineering Executive Education

Currently we do not have a higher-level program that follows the postgraduate diploma in ML and AI. We will notify you if we launch a higher-level program along similar lines.

The concepts and models taught in the diploma covers both B2C and B2B business models. The application projects such as housing price prediction, human activity recognition, and credit card fraud detection are some of the B2B business models. We urge you to participate in the discussions, contextualize the topic under discussion, and ask pointed questions to understand why and how a tool or an algorithm will apply in a B2B context.

Yes, all the concepts are relevant to businesses of all sizes. We use examples from large companies since these are easy for everyone to relate to. However, the frameworks and concepts are applicable to smaller businesses, too. Keep in mind that large companies were also small companies once, who grew owing to their successful strategies.

We urge you to participate in the discussions, contextualize the topic under discussion, and ask pointed questions to understand why and how a framework or strategy will apply in a smaller company.

Yes, the frameworks and concepts we teach are not specific to an industry or business. Each application project is just one way of using a specific algorithm or tool. As numerous examples illustrate, machine learning and artificial intelligence algorithms and concepts find their application in various other domains as well.

We urge you to participate in the discussions, contextualize the topic under discussion, and ask pointed questions to understand why and how a framework or strategy will apply in a specific industry.

Generally, professionals in the following roles are most likely to derive the maximum benefit from the diploma:

  • CXO/Chief Data Officer
  • Product/Project Manager
  • Data Engineer
  • Data Scientist
  • Software Engineer
  • Data Analyst
  • Consultant
  • Business Analyst
  • Statistician
  • Database Engineer

The diploma requires an undergraduate knowledge of statistics (descriptive statistics, regression, sampling distributions, hypothesis testing, interval estimation etc.), calculus (derivatives), linear algebra (vectors & matrix transformation) and probability (conditional probability/Bayes theorem).

The admission process will involve a short screening test on the above topics to assess participant readiness for the diploma.

Participants are required to possess an intermediate knowledge of Python since all assignments/application projects will be done using the Python programming language. Emeritus offers a complimentary Python for Data Analytics certificate course to meet this prerequisite. Participants who successfully complete this certificate course will receive a certificate of completion from Emeritus Institute of Management.

Duration and Course Fee


Program fee: USD 3600
Non-refundable application fee: USD 50 Application deadlines:
  • Round 1: 30 July 2018
Program commences: 02 Aug 2018


David Rogers

Faculty at Columbia Business School

Geoffrey Parker

Visiting Scholar and Research Fellow, MIT Initiative on the Digital Economy (IDE)


Diploma fee: USD 3,000
Application Fee: (Non-Refundable) USD 50 
Diploma Starts: 10 September 2020
Application Deadline: 08 September 2020



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Diploma fee: USD 3,000Application Fee: (Non-Refundable) USD 50 Diploma Starts: 10 September 2020Application Deadline: 08 September 2020

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