Introduction to Artificial Intelligence

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Course Dates

STARTS ON

December 9, 2021

Course Duration

DURATION

10 weeks, online
8-10 hours per week

Course Duration

PROGRAM FEE

US$2,500 US$2,250 or get US$250 off with a referral

Course Information Flexible payment available

Application Details

Round 1

US$2,250 / program fee

Application Deadline
1

Get the AI Skills to Advance Your Organization and Your Career

The challenges brought on by the pandemic have been profound, and virtually every organization and industry has felt its effects. In response, the adoption of artificial intelligence (AI) has been fast-tracked by many of today’s most forward-thinking companies. This rapid growth is expected to continue with the global spending forecast to expand by 15.2% in 2021 to nearly $342 billion, according to the International Data Corporation (IDC).

This program is designed to provide a comprehensive overview of AI in both theory and practice with a focus on modern computational techniques for representing task-relevant information and making intelligent decisions. As a participant, you’ll develop AI problem-solving skills that are applicable across a large range of information systems to help you and your organization stay competitive in this rapidly evolving landscape.

#1 in Artificial Intelligence Specialty and Graduate Programs for Computer Science

SOURCE: U.S. NEWS & WORLD REPORT

Key Outcomes

This 10-week online program is designed to provide software developers, data science professionals, and other professionals looking to move into the AI arena with the skills they need to solve problems with AI.

Participants who complete the program will be prepared to:

  • Identify reasonable representations for given AI problems.
  • Consider the use of basic symbolic and numeric techniques.
  • Apply algorithms to representations when finding solutions to AI problems
  • Examine how is incorporated into various applications.
  • Evaluate the ethical and societal implications of the field of AI.

Program Modules

Structured as an online program, content is shared via live and recorded faculty videos and office hours with learning facilitators.

Module 1:

Search

Evaluate search as a technique to find a solution to an artificial intelligence (AI) problem with goal criteria and understand the tradeoffs and potential information that can be applied to different search problems.

Module 2:

State and Action Representation

Learn to translate a problem from an English statement into something AI can understand as you construct state and action representations for complex search problems and describe how they affect search performance.

Module 3:

Constraint Satisfaction

Formulate a problem as a set of constraints and an objective and apply relevant algorithms to find a solution efficiently. Identify when the objective is to solve for a set of values and apply strategies to accomplish this goal.

Module 4:

Probability and Markov Processes

Consider that many aspects of the world are probabilistic and evaluate how to translate that in AI. Demonstrate familiarity with probabilistic approaches to AI and implement algorithms to solve probabilistic sequential decision problems.

Module 5:

Machine Learning Models

Reflect on machine learning (ML) as a popular approach employed to help businesses understand their data. Evaluate ML as a technique for finding patterns in data and explore popular ML algorithms and their corresponding data requirements.

Module 6:

Practical Machine Learning

Consider the many nuances to training and evaluating ML algorithms and explore the various ideas to help train better ML models. Analyze tuning hyperparameters that affect model performance and evaluate models properly.

Module 7:

Randomized Algorithms

Recognize that real-world problems have state spaces that are infeasible to search systematically. Demonstrate the applicability of randomized search techniques and understand when such approaches are appropriate to use.

Module 8:

Common AI Applications

Evaluate information retrieval and other AI technology behind search engines, personal assistants, and other question answering systems. Analyze recommender systems as a common AI application utilized in movie or music selection or for items to purchase.

Module 9:

Human—AI Interaction

Appreciate that AI systems do not act in a vacuum; they are typically completing tasks for or with people. Consider common approaches to integrating human feedback into AI systems and review current trends and challenges in AI systems that interact with people.

Module 10:

Autonomous Agents

Evaluate the emerging use of AI in systems that can operate autonomously. Reflect on how reliably achieving complex tasks in dynamic environments is difficult and requires the careful integration of numerous AI techniques.

Module 1:

Search

Evaluate search as a technique to find a solution to an artificial intelligence (AI) problem with goal criteria and understand the tradeoffs and potential information that can be applied to different search problems.

Module 6:

Practical Machine Learning

Consider the many nuances to training and evaluating ML algorithms and explore the various ideas to help train better ML models. Analyze tuning hyperparameters that affect model performance and evaluate models properly.

Module 2:

State and Action Representation

Learn to translate a problem from an English statement into something AI can understand as you construct state and action representations for complex search problems and describe how they affect search performance.

Module 7:

Randomized Algorithms

Recognize that real-world problems have state spaces that are infeasible to search systematically. Demonstrate the applicability of randomized search techniques and understand when such approaches are appropriate to use.

Module 3:

Constraint Satisfaction

Formulate a problem as a set of constraints and an objective and apply relevant algorithms to find a solution efficiently. Identify when the objective is to solve for a set of values and apply strategies to accomplish this goal.

Module 8:

Common AI Applications

Evaluate information retrieval and other AI technology behind search engines, personal assistants, and other question answering systems. Analyze recommender systems as a common AI application utilized in movie or music selection or for items to purchase.

Module 4:

Probability and Markov Processes

Consider that many aspects of the world are probabilistic and evaluate how to translate that in AI. Demonstrate familiarity with probabilistic approaches to AI and implement algorithms to solve probabilistic sequential decision problems.

Module 9:

Human—AI Interaction

Appreciate that AI systems do not act in a vacuum; they are typically completing tasks for or with people. Consider common approaches to integrating human feedback into AI systems and review current trends and challenges in AI systems that interact with people.

Module 5:

Machine Learning Models

Reflect on machine learning (ML) as a popular approach employed to help businesses understand their data. Evaluate ML as a technique for finding patterns in data and explore popular ML algorithms and their corresponding data requirements.

Module 10:

Autonomous Agents

Evaluate the emerging use of AI in systems that can operate autonomously. Reflect on how reliably achieving complex tasks in dynamic environments is difficult and requires the careful integration of numerous AI techniques.

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Program Experience

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Office hours with learning facilitators

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Knowledge checks

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Dedicated Program Support Team

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Case Studies

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Programming Assignments

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Discussion Boards

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Wikis/Crowdsourced Activities

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Scenario and problem-based learning

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Faculty Interaction

Who Should Attend

This program is designed for participants interested in expanding their knowledge of emerging technologies such as AI and gaining a deeper understanding of AI tools and techniques. This program may be most suitable for the following:

Software Developers/Technology Professionals seeking to gain a foundational understanding of AI processes and apply them in their work.

Representative roles include:

  • Software Engineer
  • Software Developer
  • Automation Engineer
  • Design Engineer
  • Full-Stack Developer
  • Front-End Developer
  • Back-End Developer

Data Science Professionals looking to develop an understanding of modern computational techniques for developing AI-based solutions.

Representative roles include:

  • Data Scientist
  • AI Application Engineer
  • Data Engineer
  • Senior Data Engineer

Other Professionals who want to develop decision support systems (such as medical support or recommender systems), plan and monitor AI systems, develop mixed initiatives, and bring humans into the AI loop.

Representative roles include:

  • Project Manager
  • Support Analyst
  • Information Technology professionals

PREREQUISITES: Functional knowledge of calculus, linear algebra, probability, and statistics is beneficial. Participants should also have a working-level knowledge of Python, algorithm design, and data structures.

Program Faculty

Faculty Member Stephanie Rosenthal

Stephanie Rosenthal

Assistant Teaching Professor, Carnegie Mellon University School of Computer Science

Stephanie Rosenthal is an Assistant Teaching Professor at Carnegie Mellon University School of Computer Science. She has many years of experience in higher education, teaching robotics, artificial intelligence, and applied data analytics. Her practitioner experience includes industry and government, with organizations... More info

Faculty Member Reid Simmons

Reid Simmons

Research Professor and Director of the Bachelor Science in Artificial Intelligence (BSAI), Carnegie Mellon University, School of Computer Science

Reid Simmons is a Research Professor and Director of the BSAI at Carnegie Mellon University School of Computer Science. As head of the Reliable Autonomous Systems Lab, the research investigates developing reliable, highly autonomous systems (especially mobile robots) that operate in rich, uncertain environments... More info

Program Exercises

Throughout the program, you’ll have the opportunity to apply your understanding of the content by completing computational problems and evaluating AI applications and methods in the context of practical problems. Example exercises include:

  • Define a search problem and apply it to the Candy Grab game (defining game state and actions)
  • Use adversarial search and the minimax algorithm to identify a winning strategy for the Candy Grab game and rewarding players
  • Define variables, domains, and constraints in a Constraint Satisfaction Problem (CSP)
  • Run a search to find a solution to a CSP
  • Apply heuristics (Minimum Remaining Values (MRV) and Least Constraining Value (LCV)) to a CSP

Visual Case Study: Human — AI Interaction

As part of an original visual case study, you will deploy a new AI application for use in hospitals.

  • Immerse yourself in a simulated environment where you define considerations for an AI system interacting with humans, ensuring the deployment is both usable and explainable, transparent, fair, and ethical.
  • Explore various scenarios to support decision making as you build the AI application.
  • Present a business case to deploy this AI application at the end of the module.

Certificate

Example image of certificate that will be awarded after successful completion of this program

Certificate

Upon successful completion of the program, participants will receive a verified digital certificate of completion from Carnegie Mellon University School of Computer Science Executive Education.

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Your digital certificate will be issued in your legal name and emailed to you at no additional cost, upon completion of the program, per the stipulated requirements. All certificate images are for illustrative purposes only and may be subject to change at the discretion of Carnegie Mellon University’s School of Computer Science Executive Education.

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