Course 3: Artificial intelligence and expert systems
Course agenda
In this course, you will find:
- Overview of AI, its branches and applications in various industries
- Expert systems and knowledge representation for capturing and utilizing human expertise and making knowledge-based decisions
- Machine learning (supervised learning, unsupervised learning, and reinforcement learning, deep learning and neural networks, federated learning)
- Human factors in AI based decision making, Explainable AI (XAI), enhancing interpretability and transparency of AI systems
- The adoption of AI to empower the working activities and improve working processes
- Examples of practical applications and of software solutions (AI-powered automation, integration of AI in the workflow, AI for Big Data, etc.)
Organizational info:
- This course contains 7 lessons.
- Lessons include written content.
- To complete this course, you need 180 minutes.
- You can pause the course anytime and return to where you finished whenever you want.
- You can follow the course as it flows or at your own pace, rearranging the order of the lessons.
- At the end of the course, you will take a test containing 8 questions.
- You can take the test 2 times, if necessary.
- You must score min. 85% of the correct answers to receive the official certificate of completion.
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Participants’ Pains
We know that…
By facing AI-related concepts, students may face difficulties in understanding how to capture and utilize human expertise to make knowledge-based decisions. Also, diversity and a number of machine-learning approaches can be challenging to understand.
For the integration of AI in working activities, it is necessary to understand organizational dynamics, change management, and the practical challenges associated with integrating AI into existing workflows. Understanding how to implement AI in real-world scenarios requires practical skills that may be lacking.
Following the progress in AI-related technologies and industry trends can be challenging and time-consuming, especially considering the continuous development of new algorithms and technologies. Moreover, the rapid evolution of AI introduces the pain point of potential skill obsolescence, as students may struggle to maintain relevance in the job market without ongoing training.
Participants’ Gains
So, we developed this course, in which…
This knowledge provides a foundation for understanding the broad impact of AI technologies. Understanding expert systems and knowledge representation equips students with the skills to capture human expertise in a structured manner. Mastery of machine learning concepts provides students with a strong foundation in the core techniques driving AI advancements
Understanding the adoption of AI to empower working activities and improve processes enables students to envision and contribute to the integration of AI in various industries. This knowledge is particularly relevant for the evolving workplace where AI technologies are transforming traditional workflows.
It enhances students’ employability in a variety of industries where AI technologies are increasingly becoming integral. From data science roles to AI research and development, students can pursue diverse career paths. Having a deep understanding of AI applications, students may identify opportunities for innovation and entrepreneurship.