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Mobile app development course outline
| Institution | Jomo Kenyatta University of Science and Technology |
| Course | Information Technol... |
| Year | 3rd Year |
| Semester | Unknown |
| Posted By | Jeff Odhiambo |
| File Type | docx |
| Pages | |
| File Size | 28.85 KB |
| Views | 1539 |
| Downloads | 0 |
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Description
A Mobile App Development course typically covers a comprehensive range of topics, starting with the fundamentals of mobile programming for both Android and iOS platforms. It introduces key concepts like user interface (UI) design, front-end development using languages such as Java, Kotlin, Swift, or Dart, and back-end integration with databases. Students learn to work with mobile app development environments like Android Studio and Xcode, understand mobile app lifecycle, and manage app performance. Additionally, the course dives into APIs, data storage, debugging, and testing techniques. As students progress, they explore advanced topics such as cross-platform development, security measures, and deploying apps to app stores. By the end, learners should be able to develop, test, and deploy functional mobile applications.
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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.
76 Pages
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2.02 MB
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.
1501 Views
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4.34 MB