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.
Topics covered include:
- The history of AI and why it’s one of today’s key technologies
- The role of AI in the enterprise and various industries—from medicine to automated driving
- Why data is important to both training neural networks and the steps in a data science workflow
- An introduction to supervised learning and deep learning (prior to taking a full deep learning course)
- An introduction to current hardware and software
By the end of this course, students will have practical knowledge of:
- The definition of AI, machine learning, deep learning, and the historical developments that now differentiate modern AI from AI of the past
- How AI can help solve problems in the industry today (with examples), and how it is becoming more important in enterprise computing
- The importance of datasets, data sources, problem-solving with data, and data science workflows
- The fundamentals of supervised learning and an introduction to the concepts of deep learning
- How Intel® hardware and software can be applied to solve AI problems
The course is structured around eight weeks of lectures and exercises. Each week requires 90 minutes to complete. The exercises are implemented in Python*, so familiarity with the language is encouraged (you can learn along the way.