Artificial Intelligence (AI) Course

Definition and History of AI
Discover what AI truly is, trace its history, and explore key milestones in its development.

AI Applications and Impact
Learn how AI is transforming industries like healthcare, finance, and manufacturing. Understand its role in everyday life and its impact on the workforce and economy.

Ethics and Societal Implications of AI
Delve into the ethical challenges posed by AI, including privacy concerns, bias, and the future
of work.

Problem-Solving Methods
Explore various strategies to solve complex problems using AI, including state space
representation and constraint satisfaction.

Uninformed Search Algorithms
Understand the basics of search algorithms like Breadth-First Search (BFS) and Depth-First
Search (DFS).

Heuristic Search Algorithms
Learn about advanced search techniques such as the A* algorithm and Greedy Best-First
Search.

• Propositional and Predicate Logic
Master the concepts of logic, including truth tables, logical connectives, and inference
mechanisms.
• Representing Knowledge in AI
Discover how knowledge is represented in AI systems using semantic networks, frames, and
ontologies.

• Supervised Learning
Get introduced to supervised learning concepts like regression and classification, along with
evaluation metrics.
• Unsupervised Learning
Learn about clustering and dimensionality reduction techniques such as k-Means and PCA.
• Regression and Classification
Dive into algorithms like Linear Regression, Logistic Regression, and Support Vector Machines
(SVM).

• Basics of NLP
Learn the fundamentals of NLP, including text processing and the challenges of natural
language understanding.
• Text Pre-processing and Tokenization
Master techniques like stop words removal, tokenization, and TF-IDF for text analysis.
• Sentiment Analysis and Text Classification
Explore the methods used in sentiment analysis and text classification, with real-world case
studies.

• Introduction to Reinforcement Learning
Understand the basics of reinforcement learning, including agents, environments, and
reward systems.
• Markov Decision Processes (MDP)
Dive into MDP frameworks, policy functions, and dynamic programming techniques.
• Q-Learning and Policy Gradients
Learn about Q-learning, temporal difference learning, and policy gradient methods.

• Understanding AI Ethics
Explore the ethical principles guiding AI development, addressing issues like bias and
transparency.
• AI and Society
Discuss the societal impact of AI on employment, privacy, and decision-making.
• Global AI Governance
Understand the global policies and regulations governing AI and how to develop ethical AI
frameworks.