Foundations of AI: From Problem-Solving to Machine Learning

The course bridges problem-solving, search algorithms, and knowledge representation, paving the way for Machine Learning

Artificial Intelligence (AI) has emerged as one of the most life changing technologies of our time, revolutionizing industries and reshaping the way we live and work. Rooted in the concept of developing machines with the ability to mimic human intelligence, AI has unlocked tremendous potential across various sectors, from healthcare and finance to transportation and entertainment.

This course provides a comprehensive introduction to the field of Artificial Intelligence (AI) by covering fundamental problem-solving strategies, agent-based analysis, constraint satisfaction problems, search algorithms, and knowledge representation.

Basic Problem Solving Strategies: The course starts by introducing students to various problem-solving approaches commonly used in AI. These strategies include techniques like divide and conquer, greedy algorithms, dynamic programming, and backtracking. To help students grasp these concepts, toy problems (simple, illustrative examples) are used as initial learning tools.

Agent-Based Analysis: In AI, an agent is an entity that perceives its environment and takes actions to achieve certain goals. The course delves into the concept of agents and their characteristics, such as rationality and autonomy. Students learn how agents can interact with the environment and adapt their behaviour based on feedback and observations.

Constraint Satisfaction Problems: Constraint satisfaction problems (CSPs) are a class of problems where the goal is to find a solution that satisfies a set of constraints. The course explores how to model real-world problems as CSPs and how to use various algorithms, like backtracking and constraint propagation, to efficiently find solutions.

Search Space and Searching Algorithms: One of the fundamental aspects of AI is searching through a vast space of possible solutions to find the best one. The course explains the concept of a search space, which represents all possible states of a problem and how to traverse it systematically. Students learn about uninformed search algorithms like breadth-first search and depth-first search, as well as informed search algorithms like A* search and heuristic-based techniques.

Knowledge Representation: Representing knowledge is crucial for AI systems to reason and make decisions. The course delves into two main types of knowledge representation: propositional logic and predicate logic.

Propositional Logic: This part of the course teaches students how to represent knowledge using propositions, which are simple statements that can be either true or false. They learn about logical connectives (AND, OR, NOT, etc.) and how to build complex expressions to represent relationships and rules.

Predicate Logic: Predicate logic extends propositional logic by introducing variables and quantifiers. Students learn how to express relationships and properties involving multiple entities and make use of quantifiers like "for all" and "there exists" to reason about sets of objects.

Inference and Reasoning: Once knowledge is represented, students are introduced to the process of inference, which involves deriving new information from existing knowledge using logical rules and deduction techniques. They learn how to apply inference mechanisms to reach conclusions based on the given knowledge base.

Overall, this course provides a solid foundation in problem-solving, search algorithms, and knowledge representation essential for understanding various AI techniques and applications. By the end of the course, students should be able to apply these concepts to model and solve real-world problems using AI techniques.