Artificial intelligence (AI) and machine learning algorithms are transforming systems, experiences, processes, and entire industries. It’s no wonder that business leaders see these data-driven technologies as fundamental for the future—and that practitioners fluent in both fields are in high demand.

We are fascinated by their world-changing potential, and we’ve created the Postgraduate Diploma in Machine Learning and Artificial Intelligence to help students understand the fundamentals of AI and machine learning and how to apply them to solve complex, real-world problems.

Emeritus and Columbia Engineering Executive Education

Columbia Engineering Executive Education is collaborating with online education provider Emeritus Institute of Management (Emeritus) to offer executive education courses.

An Emeritus Postgraduate Diploma contains multiple Emeritus Certificate courses created in collaboration with Columbia Engineering Executive Education, and may also include courses created independently by Emeritus. Upon successful completion, learners will be awarded a Postgraduate Diploma by Emeritus.

You can read more about the collaboration here.


Take the first step to a Global Education

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  • Starts on

    June 17, 2020

  • Duration

    9 Months, Online

    (6-8 hours per week)
  • Program Fees

    US$ 3,000*

  • * Payable in 2 equal installments
    Non-refundable application fee: USD 50

Curriculum & Faculty


Supervised Learning

  • Regression
    Maximum Likelihood, Least Squares, Regularization
  • Bayesian Methods
    Bayes Rule, MAP Inference, Active Learning
  • Foundational Classification Algorithms
    Nearest Neighbors, Perceptron, Logistic Regression
  • Refinements to Classification
    Kernel Methods, Gaussian Process
  • Intermediate Classification Algorithms
    SVM, Trees, Forests and Boosting

Unsupervised Learning

  • Clustering Methods
    K-Means Clustering, E-M, Gaussian Mixtures
  • Recommendation Systems
    Collaborative Filtering, Topic Modeling, PCA
  • Sequential Data Models
    Markov and Hidden Markov Models, Kalman Filters
  • Association Analysis
  • Clustering Methods – II
    Model Comparisons, Analysis Considerations
  • Introduction to Artificial Intelligence
  • Intelligent Agents and Uninformed Search
  • Heuristic Search
  • Adversarial Search and Games
  • Constraint Satisfaction Problems
  • Reinforcement Learning
  • Logical Agents
  • AI applications: Natural Language Processing
  • AI Applications and Course Review

Application Assignments

Market Segmentation

Market Segmentation

    You will create market segments using the US Census dataset and by applying the k-means clustering method.

Machine Learning

Machine Learning

    Apply the Data Science workflow to a classic e-commerce dataset to predict retention and customer sales over time (Amazon sales dataset)

Natural Language Processing

Natural Language Processing

    Explore text analysis, text mining, sentiment analysis with classic text data sets (i.e. Twitter, Yelp, Wikipedia) and packages such as SpaCy and NLTK

Reinforcement Learning

Reinforcement Learning

    Apply OpenAI Gym, TensorFlow, and PyTorch to train systems such as Stanford Question Answering Dataset

Constraint Satisfaction Problems

Constraint Satisfaction Problems

    Implement constraint optimization techniques in TensorFlow for Loan Approvals dataset

Adversarial Search and Games

Adversarial Search and Games

    Apply decision making across voting election data (online voting data for US elections)

Search Algorithms

Search Algorithms

    Apply advanced search techniques from Grid Search and Random Search to A* to identify parameters appropriate

Credit Card Fraud Detection

Credit Card Fraud Detection

    You will detect potential frauds using credit card transaction data. You will apply the random forest method to identify fraudulent transactions.

Human Activity Recognition

Human Activity Recognition

    You will predict the human activity (walking, sitting, standing) that corresponds to the accelerometer and gyroscope measurements by applying the nearest neighbours technique

Movie Recommendation Engine

Movie Recommendation Engine

    You will build a movie recommendation engine by applying collaborative filtering and topic modelling techniques. You use a dataset which contains 20 million viewer ratings of 27,000 movies.

House Price Prediction

House Price Prediction

    You will write code to predict house prices based on several parameters available in the Ames City dataset compiled by Dean De Cock using least squares linear regression and Bayesian linear regression.


Ansaf Salleb-Aouissi
Ansaf Salleb-Aouissi

Department of Computer Science, Columbia University

John Paisley
John Paisley

Columbia University Associate Professor, Electrical Engineering Affiliated Member, Data Sciences Institute.

Course Leaders

*Course Leaders are subject to change

Carleton Smith

Course Leader, Emeritus

Jacob Koehler

Course Leader, Emeritus

Industry Leaders

In addition to Course Leaders, industry experts focusing on data science share their knowledge and experience through periodic guest lectures.

Learning Experience

Benefits to the Learner

Enhance Your Career Capital

  • Professional acceleration through our enriched leadership toolkit.
  • Learn while you earn.
  • Get noticed. Get ahead.
  • Understand how to manage your career & personal brand.

Enhance Your Social Capital

  • Make new, life-long connections with experienced business people from a wide variety of cultures, industries, and backgrounds.
  • Inclusion in the Emeritus Network
  • Invitation to Emeritus alumni events globally including career panels, CXO speaker series, and industry interactions.

Manage Your Brand Capital

  • A Global Business Education on your resume
  • Top 10 percent of the class achieves the status of Emeritus Scholars determined by the overall diploma GPA

Deepen Your Intellectual Capital

  • World class curriculum and teaching by faculty from Columbia Engineering Executive Education.
  • Peer-to-peer learning through learning circles, classroom discussions, and project reviews.
  • Selective entrance criterion ensures you learn with the best.


Certificate Click to view certificate



The diploma requires an undergraduate knowledge of statistics, (descriptive statistics, regression, sampling distributions, hypothesis testing, interval estimation, etc.) calculus, linear algebra (vectors, matrices, derivatives), and probability.


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 Science certificate course to meet this prerequisite. Participants who successfully complete this certificate course will receive a certificate of completion from Emeritus Institute of Management.


  • You can pay for the course either with an international debit or credit card, or bank wire transfer. On clicking the apply now button, you will be directed to the application form and the payment page.
  • We provide deferrals and refunds in specific cases. The deferrals and refund policy is available here.
  • You will be provided a course login within 48 hours of making a payment.


Please provide your work experience and your current employer via the application.


Application Requirements

  • Minimum three years of professional work experience
  • Employment history (CV/resume)
  • University transcripts
  • All candidates who have received their bachelor’s or other degree or diploma from an education institution where English is NOT the primary language of instruction are required to demonstrate English language proficiency through ANY ONE of the following methods
    – Obtain a TOEFL minimum score of 550 for the paper based test or its equivalent
    – Obtain an IELTS minimum score of 6.0 Obtain a Pearson Versant Test minimum score of 59
    – Obtain a Certificate of Completion for a Certificate course offered by the Emeritus Institute of Management
    – Submit a document which shows that the candidate has, for the last 24 months or more, worked in ANY ONE of these countries: Antigua and Barbuda, Australia, The Bahamas, Barbados, Belize, Canada, Dominica, Grenada, Guyana, India, Ireland, Jamaica, New Zealand, Singapore, South Africa, St Kitts and Nevis, St Lucia, St Vincent and the Grenadines, Trinidad and Tobago, United Kingdom, United States of America
  • A completed Application Form
  • Proof of diploma/degree in any field of study (your highest qualification should be submitted)


USD 3000 Payable in 2 equal installments


USD 50


17 June 2020


15 June 2020


We have listed two type of FAQs:

  • FAQs common to all courses. These are available at COMMON FAQs
  • Course specific FAQs


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