Duration
Format
6 lections + test
Video course on record
Сertificate
Document
Сourse program
Lesson 1. Introduction
Lesson 2. Formalization
Lesson 3. General Problems
Lesson 4. Almost Deep Learning
Lesson 5. Practice
Lesson 6. Final
Lesson 2. Formalization
Lesson 3. General Problems
Lesson 4. Almost Deep Learning
Lesson 5. Practice
Lesson 6. Final
Course Content
Part One Introduction:
• What is AI?
• Past, present, and future of AI
Part Two: Fundamentals of AI
• Machine learning
• Natural language processing
• Deepfakes
• Cognitive modelling
• Neural networks and large language models (LLMs)
Part Three: AI and Business
• AI strategy
• AI as the new user interface
• Industry use cases
Part Four: Ethics and AI
• AI in the workplace: application areas and ethical concerns
• AI challenges
• What is AI?
• Past, present, and future of AI
Part Two: Fundamentals of AI
• Machine learning
• Natural language processing
• Deepfakes
• Cognitive modelling
• Neural networks and large language models (LLMs)
Part Three: AI and Business
• AI strategy
• AI as the new user interface
• Industry use cases
Part Four: Ethics and AI
• AI in the workplace: application areas and ethical concerns
• AI challenges
Learning Outcomes
Upon completing the course, students will be able to:
• Explain the meaning and origins of AI.
• Identify the characteristics, limitations, and ethical considerations of AI.
• Discuss current and future developments in AI and its applications.
• Construct simple state spaces and select appropriate search techniques.
• Apply knowledge representation using logical, semantic network, frame, and production
rules.
• Explain the concept and sources of uncertainty, and apply probabilistic reasoning.
• Explain the meaning and origins of AI.
• Identify the characteristics, limitations, and ethical considerations of AI.
• Discuss current and future developments in AI and its applications.
• Construct simple state spaces and select appropriate search techniques.
• Apply knowledge representation using logical, semantic network, frame, and production
rules.
• Explain the concept and sources of uncertainty, and apply probabilistic reasoning.
Teachers
Smolkina Iuliia
Lecturer and author of courses on data science and artificial intelligence at Moscow Institute of Physics and Technology and Higher School of Economics.
His expertise lies in developing digitalization solutions for large companies, including through the use of AI.
His expertise lies in developing digitalization solutions for large companies, including through the use of AI.
25 $ (1 500 ₽)
Access to materials is maintained for one year.
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