Artificial Intelligence Course Syllabus
The Artificial intelligence is the imitation of human intellect courses by machines, especially computer systems. Exact applications of AI include expert systems, natural language processing, speech recognition, and computer vision.
How does AI work?
- As the hype around AI has accelerated, sellers have gone out of their way to promote how their products and services use AI. Often, AI is simply a component of AI, such as machine learning.
- Artificial intelligence requires a foundation of specialized hardware and software to write and train machine learning algorithms. No programming language is synonymous with AI, but some, including Python, R, and Java, are popular.
- AI systems generally work by taking large amounts of labeled training data, analyzing the data for correlations and patterns, and utilizing these patterns to make predictions about future states.
- This way, a chatbot that receives examples of text chats can learn to produce real connections with people. A copy recognition tool can learn to identify and define objects in images by reviewing millions of examples.
AI programming focuses on three cognitive abilities: learning, reasoning, and self-correction.
They are learning processes: This aspect of AI programming focuses on data acquisition and creating rules to turn data into actionable insights. The algorithms provide computing devices with step-by-step instructions on how to complete a specific task.
Reasoning processes: This aspect of AI programming emphasizes choosing the correct algorithm to achieve the desired result.
Self-correction procedures: This aspect of AI programming is designed to continuously fine-tune the algorithms to ensure they provide the most accurate results possible.
Artificial Intelligence Syllabus 2022
Here is the diverse Artificial Intelligence Course Syllabus taught commonly at all the course levels of study: Diploma, Undergraduate and Postgraduate.
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Introduction to Artificial Intelligence Introduction to Machine Learning Introduction to Deep Learning Web Technologies Database Management Systems Programming Discrete Structures Computer Networks Interface Technologies Signal Processing Introduction to Data Science Computation Theories Cryptography Internet of Things (IoT)
BTech Artificial Intelligence Syllabus
Below is the tabular representation of the BTech Artificial Intelligence Syllabus, taught almost commonly in all the top institutions.
| CAD and Drafting | Programming and Problem-solving |
| Introduction to AI | Advanced Physics |
| Engineering Chemistry | Mechanical Workshop |
| Discrete Structures | Computer Networks |
| Design and Analysis of Algorithms | Software Engineering and Testing Methodologies |
| Principles of Operating System | Intro to Biology for Engineering |
| Introduction to Deep Learning | Robotics and Intelligent Systems |
| OOPS using JAVA | Management Studies |
| Web Technologies | Compiler Design |
| Theory of Computation | Neural Networks |
MTech Artificial Intelligence Syllabus
A tabular representation of MTech Artificial Intelligence is provided in the table below:
| AI and Artificial Neural Networks | Embedded Systems |
| AI-Based Programming Tools | Soft Computing |
| Knowledge Engineering and Expert Systems | Machine Learning |
| Human-Computer Interaction | Natural Language Processing |
| Image Processing and Machine Vision | Automated Reasoning |
| Signal Processing | Interface Technologies |
| Speech or Biometric Processing | Computational Intelligence |
| Agent-Based Intelligence Systems | Modeling and Simulation of Digital Systems |
| Problem-solving Methods | Cognitive Systems |
| Summer Internships | Projects |
