Detailní popis

Building on the foundational knowledge acquired in Artificial Intelligence in the World, you'll embark on a thrilling journey through the world of artificial intelligence. From machine learning to robotics, you'll gain hands-on experience developing AI systems, delve into the complexities of natural language processing and computer vision, and consider the ethical and societal implications of this rapidly evolving technology. Get ready to be amazed and inspired by the limitless possibilities of AI as you immerse yourself in this cutting-edge field.

 Students are strongly recommended to study on average 4 hours per week.

 PREREQUISITES: Artificial Intelligence in the World (recommended)

Struktura kurzu

Module 1: Power of Perception
01.00 Introduction to Power of Perception
01.01 Advancements in AI: How AI is used in different areas
01.02 Dig Deeper with AI: AI systems and how they are used
01.03 Make the Connection: Relationship between AI, humans, and the environment
01.04 Challenge Accepted: How training data influences learning
01.05 Dive in with Data: The process of collecting and analyzing data for AI
01.06 Data Wrangling: Databases and how they are used

Module 2: How to Train Your Computer
02.00 Introduction to How to Train Your Computer
02.01 Data-Driven Intelligence: Relational databases and how they are helpful for structured data
02.02 Good Deeds: Identifying challenges and solutions
02.03 Solution Finder: Software development life cycle (SDLC)
02.04 Fair, Accurate, and Ethical: Understanding datasets and how they are used in decision-making
02.05 It’s All Part of the Process: How AI can inform and drive decision-making and automation
02.06 Dataset, Data-match, Data-game!: Data patterns and processing

Module 3: It’s Your Story
03.00 Introduction to It’s Your Story
03.01 Starring AI as the Problem Solver: Supervised and unsupervised learning
03.02 Sensing Gathers Data; Perception Makes Sense of It: Speech transcription and visual object recognition
03.03 Choose Wisely: Training machine learning (ML) models
03.04 Data—It\'s a Private Practice: Developing AI models
03.05 It’s Alive: Embedded systems
03.06 What’s Next?: Career and technical student organizations

Sylabus kurzu

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