Artificial Intelligence

About Course

An Artificial Intelligence (AI) course offers a comprehensive introduction to the fundamentals of AI, including machine learning, neural networks, and natural language processing. Students explore how AI systems are designed and trained to perform tasks that typically require human intelligence, such as recognizing images, understanding speech, and making decisions. The course often combines theoretical knowledge with practical applications, allowing students to build and experiment with AI models.

In addition to technical skills, an AI course also emphasizes ethical considerations, such as fairness, transparency, and the impact of AI on society. Students learn to evaluate the broader implications of AI technologies, including potential biases and privacy concerns. This balanced approach ensures that graduates are not only proficient in AI development but also equipped to navigate the complex ethical landscape that surrounds the deployment of AI in various industries.

Course Content

Module 1: Introduction to AI

Definition, history, and scope of artificial intelligence.

00:00
Fundamental concepts: agents, environments, and problem-solving paradigms.

00:00
AI applications in industry: robotics, NLP, and expert systems.

00:00

Module 2: Search Algorithms and Heuristics

Module 3: Knowledge Representation and Reasoning

Module 4: Machine Learning and AI Planning

Module 5: Natural Language Processing (NLP)

Module 6: Computer Vision and Image Processing

Module 7: Project 1: Developing an AI-based Chatbot

Module 8: Project 2: AI-Powered Image Recognition System

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Artificial Intelligence

About Course

Course Content

Module 1: Introduction to AI

Definition, history, and scope of artificial intelligence.

Fundamental concepts: agents, environments, and problem-solving paradigms.

AI applications in industry: robotics, NLP, and expert systems.

Module 2: Search Algorithms and Heuristics

Problem-solving using uninformed search: BFS, DFS, and uniform cost search.

Informed search strategies: A*, greedy search, and heuristic functions.

Constraint satisfaction problems (CSP) and optimization techniques.

Module 3: Knowledge Representation and Reasoning

Logical reasoning: propositional and first-order logic.

Semantic networks, frames, and ontologies for knowledge representation.

Reasoning under uncertainty: Bayesian networks and Markov models.

Module 4: Machine Learning and AI Planning

Introduction to AI planning: STRIPS, state-space search, and plan generation.

Reinforcement learning: Markov decision processes (MDPs) and Q-learning.

Integration of ML models in AI systems for adaptive learning.

Module 5: Natural Language Processing (NLP)

NLP basics: text preprocessing, tokenization, and stemming/lemmatization.

Language models: n-grams, Word2Vec, and transformers (BERT, GPT).

Applications: sentiment analysis, machine translation, and chatbots.

Module 6: Computer Vision and Image Processing

Fundamentals of image processing: filtering, edge detection, and image segmentation.

Introduction to convolutional neural networks (CNNs) and object detection.

Advanced topics: image classification, facial recognition, and generative models.

Module 7: Project 1: Developing an AI-based Chatbot

Design and implementation of a chatbot using NLP techniques.

Integration with a messaging platform (e.g., Slack, Telegram).

Evaluation and improvement of chatbot performance.

Module 8: Project 2: AI-Powered Image Recognition System

Building a computer vision model for object detection or image classification.

Dataset collection, model training, and performance evaluation.

Deployment of the model as a web or mobile application.

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About Course

Course Content

Module 1: Introduction to AI

Definition, history, and scope of artificial intelligence.

Fundamental concepts: agents, environments, and problem-solving paradigms.

AI applications in industry: robotics, NLP, and expert systems.

Module 2: Search Algorithms and Heuristics

Problem-solving using uninformed search: BFS, DFS, and uniform cost search.

Informed search strategies: A*, greedy search, and heuristic functions.

Constraint satisfaction problems (CSP) and optimization techniques.

Module 3: Knowledge Representation and Reasoning

Logical reasoning: propositional and first-order logic.

Semantic networks, frames, and ontologies for knowledge representation.

Reasoning under uncertainty: Bayesian networks and Markov models.

Module 4: Machine Learning and AI Planning

Introduction to AI planning: STRIPS, state-space search, and plan generation.

Reinforcement learning: Markov decision processes (MDPs) and Q-learning.

Integration of ML models in AI systems for adaptive learning.

Module 5: Natural Language Processing (NLP)

NLP basics: text preprocessing, tokenization, and stemming/lemmatization.

Language models: n-grams, Word2Vec, and transformers (BERT, GPT).

Applications: sentiment analysis, machine translation, and chatbots.

Module 6: Computer Vision and Image Processing

Fundamentals of image processing: filtering, edge detection, and image segmentation.

Introduction to convolutional neural networks (CNNs) and object detection.

Advanced topics: image classification, facial recognition, and generative models.

**Module 7: **Project 1: Developing an AI-based Chatbot

Design and implementation of a chatbot using NLP techniques.

Integration with a messaging platform (e.g., Slack, Telegram).

Evaluation and improvement of chatbot performance.

**Module 8: **Project 2: AI-Powered Image Recognition System

Building a computer vision model for object detection or image classification.

Dataset collection, model training, and performance evaluation.

Deployment of the model as a web or mobile application.

Student Ratings & Reviews

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Module 7: Project 1: Developing an AI-based Chatbot

Module 8: Project 2: AI-Powered Image Recognition System