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 manifold in the coming years. In fact, as per a survey conducted by PwC, business leaders said they believe AI is going to be fundamental in the future. In fact, 72% termed it a “business advantage”.
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.
- Faculty Video Lectures
- Moderated Discussion Boards
- Application Projects
- Q&A Sessions with Course Leaders
- Live Online Teaching
- 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
- 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.
- This course is designed for working professionals and requires proficiency in English. All videos are recorded in English. All assignments are written in English and are required to be responded to in English.
- The course requires you to have any device with 1 Mbps (or more) Internet connection. The laptop should support one of the following browsers: Chrome 71, Firefox 64, IE 11, Edge 42, Safari 11.
BENEFITS TO THE LEARNER
- Global Business Education
- Rigorous and experiential curriculum
- World-renowned faculty
- Globally Connected Classroom: Peer to Peer Learning Circles
- Action Learning: Learning by Doing
- Certificate from EMERITUS in collaboration with Columbia
Engineering Executive Education
- Build new networks through peer interaction
- Benefit from diverse class profiles
- Professional Acceleration through our enriched leadership toolkit
- Learn while you earn
- Get noticed. Get ahead.
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