APPLIED ARTIFICIAL INTELLIGENCE
Artificial Intelligence (AI) is being used extensively to solve real-world complex problems. From driving automobiles to providing virtual assistance, use of artificial intelligence in our day to day lives are projected to increase. Most business leaders surveyed state that AI will fundamentally transform their business.
At Columbia Engineering, we are fascinated by the possibilities of data-driven technologies. We have created the Applied Artificial Intelligence course, in partnership with EMERITUS, to help students across the world understand how this data-centric approach can be applied to your daily lives.
WHO IS THIS COURSE FOR?
This course is designed for professionals who intend to transition to the role of an AI specialist. This course is for you if you are Software developer or a Project manager or a Business analyst or a Data Scientist or a Data Engineer who wants to build a solid foundation in Artificial Intelligence.
Previous batches have come from:
- Industries: Banking, Software, Consulting, Retail, Consumer Packaged Goods, Healthcare and Energy industries.
- Countries: United States, India, United Kingdom, Canada, Australia, Hong Kong, Mexico.
WHAT ARE THE LEARNING OUTCOMES?
At the end of this course, participants will learn to:
- Learn the essentials of creating intelligent systems
- Understand and implement the 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
EMERITUS and Columbia Engineering Executive Education
Columbia Engineering Executive Education is collaborating with online education provider EMERITUS to offer executive education courses.
An EMERITUS Certificate course created in collaboration with Columbia Engineering Executive Education is based on syllabus approved by Columbia Engineering Executive Education, and contains video content created and recorded by Columbia Engineering Executive Education faculty, combined with assessments, assignments, projects, cases, and exercises delivered by EMERITUS. Upon successful completion of the course, learners will be awarded a certificate jointly by EMERITUS and Columbia Engineering Executive Education.
You can read more about the collaboration here.
- Overview of AI
- Applications of AI
- AI foundation and history
- Intelligent agents
- Search agents
- Uninformed search
- Uninformed search examples
- Heuristics and greedy search algorithm
- A* search and optimality
- Search algorithms recap
- Local search
- Adversarial search and games
- Minimax algorithm
- Alpha-beta pruning
- Stochastic games
- Machine learning concepts
- K-nearest neighbors and training-testing
- Overfitting-underfitting and regularization
- Linear models for regression
- Machine learning: perceptron
- Logistic regression
- Decision trees
- Naïve Bayes
- Ensemble methods
- Neural networks
- Association rules
- Constraint satisfaction problems
- Cryptarithmetic puzzle
- Constraint propagation
- Problem structure
- Reinforcement Learning Introduction
- Reinforcement learning overview
- Markov decision process (MDP)
- Example of an MDP and Bellman equations
- Value function – Matrix notation
- Finding optimal policy in MDPs – iterative methods
- Policy iteration method example
- Value iteration method
- Reinforcement learning – algorithms
- Knowledge-based agents
- The Wumpus world
- Logical agent
- Inference rules
- Reduced Wumpus world
- Model checking and inference
- Theorem proving and proof by resolution
- Conversion to CNF and resolution algorithm
- Forward and backward chaining
- Propositional logic: summary
- First order logic
- AI Applications: Natural language processing
- Text classification
- Language models
- Progress in NLP
- Deep learning: background and history
- Deep learning: architecture and application
- Introduction to robotics
- Robot path planning – visibility graphs
- Voronoi graphs and potential fields
- Probabilistic roadmap planner (PRM)
- Rapidly-exploring random tress (RRT) and path planning summary
Department of Computer Science, Columbia University
EMERITUS follows a unique online model. This model has ensured that nearly 90 percent of our learners complete their course.
- Orientation Week
The first week is orientation week. During this week you will be introduced to the other participants in the class from across the world. You will also learn how to use the learning platform and other learning tools provided.
- Weekly Goals
On other weeks, you have learning goals set for the week. The goals would include watching the video lectures and completing the assignments. All assignments have weekly deadlines.
- Recorded Video Lectures
The recorded video lectures are by faculty from the collaborating university.
- Live Webinars
Every few weeks, there are live webinars conducted by EMERITUS course leaders. Course leaders are highly-experienced industry practitioners who contextualize the video lectures and assist with questions you may have regarding your assignments. Live webinars are usually conducted between 1 pm and 3 pm UTC on Tuesdays and Wednesdays.
- Clarifying Doubts
In addition to the live webinars, for some courses, the course leaders conduct Office Hours, which are webinar sessions that are open to all learners. During Office Hours, learners ask questions and course leaders respond. These are usually conducted every alternate week to help participants clarify their doubts pertaining to the content.
The EMERITUS Program Support team members will follow up and assist over email and via phone calls with learners who are unable to submit their assignments on time.
- Continued Course Access
You will continue to have access to the course videos and learning material for up to 12 months from the course start date.
EMERITUS Program Support Team
If at any point in the course you need tech, content or academic support, you can email program support and you will typically receive a response within 24 working hours or less.
You can access EMERITUS courses on tablets, phones and laptops. You will require a high-speed internet connection.
On completing the course you join a global community of 5000+ learners on the EMERITUS Network. The Network allows you to connect with EMERITUS past participants across the world.
ADMISSION & FEES
DURATION AND COURSE FEE
- Starts TBD
- 3 Months
- 6-8 hours per week
- Course Fees USD 1400
- This course requires an undergraduate knowledge of linear algebra (vectors, matrices, derivatives), calculus, and basic probability theory.
- All assignments/application projects will be done using the Python programming language. You should have an intermediate knowledge of Python or you should have completed the Emeritus Python for Data Science course prior to joining this course.
- You can pay for the course either with an international debit or credit card (unfortunately we are unable to accept Diners credit cards), or through a bank wire transfer. On clicking the apply now button below, 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.
We have listed two type of FAQs.
1. Course specific FAQs
2. FAQs common to all courses.
Course specific FAQs are provided below. FAQs common to all courses are available at COMMON FAQs
COURSE SPECIFIC FAQs
- Applied Machine Learning:
Teaches you the essential statistical tools and methods, and algorithms that can help you create models that can analyse vast amount of data to predict outcomes that can be immensely useful for your personal and business ventures alike. By working on the real-life application projects, you also acquire the knowledge of how different algorithms are used in different kinds of industry scenarios.
- Applied Data Science:
Teaches you the essentials of data science – from extraction, visualization to analysis and insights. Via EDA, this course will let you discover the underlying patterns in the vast quantity of data, and let you answer the whys and whats about those data points using hypothesis testing. In this course you will learn to use foundational ML algorithms to derive sentiments from text, group data points or split datasets to find insights.
- Applied Artificial Intelligence:
Teaches you to the essentials of creating intelligent systems. Starting with the foundation of AI, this course teaches you the 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.
With respect to the growth and demand in the artificial intelligence (AI) field, a white paper 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.
Glassdoor estimates that average salaries for AI-related jobs advertised on company career sites rose 11 percent between October 2017 and September 2018 to USD $123,069 annually.
These reports serve as a credible source about the demands for skills related to AI. Even though currently you have no directly relevant experience in AI, there is no denying that given the growth in the field, you will come across opportunities to use your existing industry knowledge and the new skills acquired via our Applied Artificial Intelligence course.
We are unable to comment about the specific details of courses from other providers. We would encourage you to list the things you are looking for in a course and then compare our course with other providers’ courses. We have listed some of the parameters our learners have found relevant.
|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||Prof. Ansaf Salleb-Aouissi received her PhD in Computer Science from the University of Orleans, France.
Ansaf’s research interests lie in machine learning and AI. 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.
|Course Fee||1,400 USD|
|Credential||Certificate in Applied Artificial Intelligence in Data Science from EMERITUS in collaboration with Columbia Engineering Executive Education|
After you complete your certificate course in artificial intelligence, you can take up the Postgraduate Diploma in Machine Learning and Artificial Intelligence due to launch in June 2019.
The concepts and models taught in the course cover 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, data science 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 more 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 program:
- CXO/Chief Data Officer
- Product/Project Manager
- Data Engineer
- Data Scientist
- Software Engineer
- Data Analyst
- Business Analyst
- Database Engineer