AI Teaching

Teaching at the Universities and Companies

This will be through Limitless Learning Institute

  • For Employees: Masters/Engineers - Integrate AI in engineering projects

  • For the Company: AI integration at Work

The curriculum is structured around 8 to 12 weeks of lectures and exercises. Each week requires ~3 hours to complete. The exercises are implemented in Python, so familiarity with the language is encouraged, though not required (students can learn along the way). A certificate of completion will be delivered from Intel AI Academy after passing exam.

AI Teaching Bundles | AI Limitless Paths

Artificial Intelligence Curriculum

Introduction to AI

This course is for developers, students, or industry professionals from other computer science and engineering fields who are curious about AI. Explore AI—what it's used for and why—without the math that is involved with later courses.

Machine Learning

This course will provide an overview of the fundamentals of machine learning. Students will learn about the type of problems that can be solved, the building blocks and the fundamentals of building models in machine learning. A number of key algorithms will be explored. By the end of this course, students will leave with practical knowledge in a number of supervised learning algorithms along with an understanding of key concepts like under and overfitting, regularization, and cross validation. Students will be able to identify the type of problem they’re trying to solve, choose the right algorithm, tune parameters, and validate a model.

The curriculum is structured around 12 weeks of lectures and exercises. Each week requires ~3 hours to complete. The exercises are implemented in Python*, so familiarity with the language is encouraged, though not required (students can learn along the way).

Deep Learning

This course provides an introduction to deep learning. Deep learning has gained significant attention in the industry by achieving state of the art results in computer vision and natural language processing. Students will learn the fundamentals of deep learning and modern techniques to build state of the art models.

By the end of this course, students will have a firm understanding of the techniques, terminology, and mathematics of deep learning. They’ll know fundamental neural network architectures, feed-forward networks, convolutional networks, and recurrent networks, and how to appropriately build and train these models. An understanding of various deep learning applications will be gained. Finally, students will be able to use pre-trained models for best results.

Ethics in AI

There is no doubt that AI presents an opportunity for radical advancements in many fields such as agriculture, manufacturing, medicine, computer science and cybersecurity, to name a few. However, with each advancement comes a number of questions and concerns, ranging from business to legal to ethical implications. There is no easy answer to these questions, but they cannot be ignored.

This module will center around the advent of AI and how the business community, and any community for that matter, should view AI from an ethical and legal viewpoint.

Key Takeaways:

  • Consider the ethical and legal implications of using AI systems

  • Be better equipped to evaluate business decisions related to AI

  • Gain a deeper understanding of the impact of bias and computational errors in AI

Data Analytics

Data is the fuel of AI. Artificial Intelligence projects do not follow the same precepts as traditional programming projects. While they can use similar agile processes, they are not designed to add features and functions as they mature, but rather to use data to improve accuracy. Learn how to identify data readiness for your potential AI project and to optimize the process of using data to solve the problem.

Key Takeaways:

  • Understanding the major ways to use AI and how data is used with each one

  • How to identify projects whose data makes them conducive to the use of AI

  • Determining the difference between data analytics and text analytics

  • Exploring the process of model improvement using data