Nanodegree Program syllabus
| Institution | Jomo Kenyatta University of Science and Technology |
| Course | Information Technol... |
| Year | 3rd Year |
| Semester | Unknown |
| Posted By | Jeff Odhiambo |
| File Type | |
| Pages | 13 Pages |
| File Size | 869.93 KB |
| Views | 1427 |
| Downloads | 0 |
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Description
A Nanodegree program syllabus typically outlines a comprehensive curriculum designed to equip students with specialized skills in fields like data science, artificial intelligence, programming, or digital marketing. The syllabus includes a structured sequence of courses covering foundational concepts, practical applications, and advanced techniques. Students are guided through hands-on projects, real-world case studies, and assessments, allowing them to build a portfolio of work that demonstrates their proficiency. The program often includes mentorship, career support, and opportunities to interact with a community of learners, ensuring students gain both technical expertise and the soft skills necessary for success in their chosen industry.
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BIT 2319: Artificial Intelligence
Institution: Jomo Kenyatta University of Science and Technology
Year: 2021/2022
Semester: 3rd Year, 1st Semester (3.1)
BIT 2319: Artificial Intelligence
Institution: Jomo Kenyatta University of Science and Technology
Year: 2021/2022
Semester: 3rd Year, 1st Semester (3.1)
BIT 2319: Artificial Intelligence
Institution: Jomo Kenyatta University of Science and Technology
Year: 2021/2022
Semester: 3rd Year, 1st Semester (3.1)
BIT 2319: Artificial Intelligence
Institution: Jomo Kenyatta University of Science and Technology
Year: 2021/2022
Semester: 3rd Year, 1st Semester (3.1)
BIT 2319: Artificial Intelligence
Institution: Jomo Kenyatta University of Science and Technology
Year: 2022/2023
Semester: 3rd Year, 1st Semester (3.1)
BIT 2319: Artificial Intelligence
Institution: Jomo Kenyatta University of Science and Technology
Year: 2022/2023
Semester: 3rd Year, 1st Semester (3.1)
BIT 2319: Artificial Intelligence
Institution: Jomo Kenyatta University of Science and Technology
Year: 2022/2023
Semester: 3rd Year, 1st Semester (3.1)
Review of Data Structures
A review of data structures involves examining various ways to organize, store, and manage data efficiently for different computational tasks. It covers fundamental structures like arrays, linked lists, stacks, and queues, as well as more complex ones like trees, graphs, and hash tables. Each data structure has unique characteristics, advantages, and use cases, influencing factors such as time complexity, memory usage, and ease of implementation. The review typically includes analyzing operations like insertion, deletion, searching, and sorting, as well as their efficiency in different scenarios. Understanding data structures is crucial for optimizing algorithms and improving software performance.
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Introduction to Artificial Intelligence
Introduction to Artificial Intelligence (AI) explores the development of computer systems that can perform tasks requiring human-like intelligence, such as problem-solving, learning, reasoning, and decision-making. AI encompasses various subfields, including machine learning, natural language processing, computer vision, and robotics. It relies on algorithms and models that enable computers to analyze data, recognize patterns, and make predictions or decisions with minimal human intervention. AI is widely used in industries such as healthcare, finance, and automation, transforming how technology interacts with the world. Understanding AI principles is essential for leveraging its potential and addressing ethical and societal challenges.
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3.61 MB
AI and the Design of Agents
"I and the Design of Agents" explores the relationship between human intelligence and the creation of artificial agents capable of autonomous decision-making. It examines how human cognition, reasoning, and problem-solving inspire the development of AI-driven agents that can perceive their environment, process information, and take actions to achieve specific goals. This topic delves into agent architectures, decision-making models, and learning mechanisms that enable adaptability and interaction with dynamic environments. Understanding this connection helps in designing intelligent systems that can collaborate with humans, solve complex tasks, and function effectively in real-world applications.
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4.34 MB