In collaboration with
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.
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.
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.
You will predict the human activity (walking, sitting, standing) that corresponds to the accelerometer and gyroscope measurements by applying the nearest neighbours technique.
You will detect potential frauds using credit card transaction data. You will apply the random forest method to identify fraudulent transactions.
You will create market segments using the US Census dataset and by applying the k-means clustering method.
Apply advanced search techniques from Grid Search and Random Search to A* to identify parameters appropriate
Apply decision making across voting election data (online voting data for US elections)
Apply the Data Science workflow to a classic e-commerce dataset to predict retention and customer sales over time (Amazon sales dataset)
Implement constraint optimization techniques in TensorFlow for Loan Approvals dataset
Apply OpenAI Gym, TensorFlow, and PyTorch to train systems such as Stanford Question Answering Dataset
Explore text analysis, text mining, sentiment analysis with classic text data sets (i.e. Twitter, Yelp, Wikipedia) and packages such as SpaCy and NLTK
Department of Computer Science, Columbia University
Columbia University Associate Professor, Electrical Engineering Affiliated Member, Data Sciences Institute.
*Course Leaders are subject to change
Course Leader, Emeritus
Course Leader, Emeritus
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 eligibility 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.
Please provide your work experience and your current employer via the application.
USD 3300 Flexible payment options available
03 December 2020
01 December 2020
We have listed two type of 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.
|Type||SPOC (Small Private Online Course) — typically cohorts of 150 people, with individualized attention|
|Delivery||Fixed start and end dates|
|Grading||All quizzes and assignments are graded|
|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 $3,300|
|Credential||Postgraduate diploma in ML & AI from Emeritus in collaboration with Columbia Engineering Executive Education|