The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events Specialist
TBD
9 months, online
6-8 hours per week
Our participants tell us that taking this program together with their colleagues helps to share common language and accelerate impact.
We hope you find the same. Special pricing is available for groups.
The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events SpecialistBased on the information you provided, your team is eligible for a special discount, for Postgraduate Diploma in Machine Learning and Artificial Intelligence starting on TBD .
We’ve sent you an email with enrollment next steps. If you’re ready to enroll now, click the button below.
Have questions? Email us at group-enrollments@emeritus.org.Apply before Month Date, Year and avail an early bird tuition assistance of US$400. Use code DIP400EBTA during payment.
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.
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 digital certificate of completion from Emeritus.
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 (E-Learning), to help students understand the fundamentals of AI and machine learning and how to apply them to solve complex, real-world problems.
The average Teacher-Student ratio in the program is 1:300.
Video Lectures
Discussions
Assignments / Application Projects
Capstone Project
Live Online Teaching Sessions
SUPERVISED LEARNING
UNSUPERVISED LEARNING
The diploma requires learners to work on application assignments, which require learners to apply the concepts they have learned to datasets and derive inferences. These assignments are intentionally made to be challenging and we expect learners to spend substantial time and effort solving them. At the end of the diploma, we expect learners to be able to apply the concepts to solve many of the business problems they face at their workplace.
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
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John W. Paisley
Ansaf Salleb-Aouissi
John W. Paisley
Columbia University Associate Professor, Electrical Engineering Affiliated Member, Data Sciences Institute
John 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.
Ansaf Salleb-Aouissi
Department of Computer Science, Columbia University
Ansaf 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.
Her current research interest includes crowd sourcing, medical informatics and education.
Course Leader, Emeritus
Course Leader, Emeritus
Upon successful completion of the diploma, participants will receive a verified digital diploma from Emeritus Institute of Management.
Download BrochureOn successful completion of this program, join a community of learners on the Emeritus Network. The Emeritus Network is your platform to connect to a global network of individuals. Benefits of the Emeritus Network include:
Applicants must be at least 21 years of age and will be required to submit:
ENGLISH LANGUAGE PROFICIENCY REQUIREMENT
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