AI Mastery: From Search Algorithms to Advanced Strategies

Master the basics of trees: roots, nodes, leaves, siblings, paths, depth, height, and degree; compare binary trees with binary search trees and learn linear and linked representations and traversals.

Explore graphs by defining vertices and edges, including directed and undirected forms. Learn BFS and DFS traversals, adjacency representations, and criteria for connectivity, cycles, and acyclicity.

Explore general search algorithms and how state space, initial and goal states, and solution paths guide problem solving agents through forward, backward, systematic, and heuristic searches.

Explore uninformed search methods, including breadth first search, uniform cost search, depth first search, and depth limited search, with practical, example‑driven explanations.

Explore informed search methods, including generate and test, best first search, and a* search, using heuristics and open and closed lists to guide problem solving.

Explore the minimax algorithm in adversarial search for two-player games, using depth-first search to evaluate terminal nodes and backtrack values, with alpha-beta pruning introduced to improve efficiency.

Master alpha beta pruning, an efficient refinement of the min max algorithm that prunes branches using alpha and beta thresholds to reduce time complexity.

Explore how constraint satisfaction problems constrain the search space with unary, binary, and higher-order constraints, using mathematical representations and real-life examples such as timetable scheduling, resource allocation, and network configuration.

Explore cryptarithmetic puzzles as constraint satisfaction problems, assigning unique digits to letters, applying constraints, and using backtracking with heuristics like minimum remaining values to solve send more money.

Learn constraint satisfaction problems, modeling with a constraint graph, and use backtracking, forward checking, and heuristics like minimum remaining values and intelligent backtracking for room coloring.

Explore intelligent agents that perceive with sensors, think and act with actuators, and maximize performance from percept sequences through rational decision making.

Explore the rational agent by mapping percept sequences to actions with an agent function, emphasizing autonomy, rationality, and performance measures tied to environment and actions.

Explore the types of agents: table driven, simple reflex, model based reflex, goal based, utility based, and learning, and how sensors, actuators, and an internal model drive perception and action.

Explore how knowledge representation enables AI to reason, infer, learn, and communicate using semantic networks, frames, and knowledge graphs, while addressing dynamic handling and privacy concerns.

Explore knowledge-based agents that use structured knowledge bases and deduction to reason and act, then analyze the four-by-four Wumpus world with pits, gold, and percepts like breeze, stench, and glitter.

Explore propositional logic, using atomic sentences p and q, with negation, conjunction, disjunction, and implication; build knowledge bases and use truth tables, forward or backward chaining, and resolution.

Explore how unification uses substitutions to make two atomic sentences identical, producing the most general unifier and outlining the algorithm and its success and failure conditions.

Explore knowledge representation using rules, detailing a knowledge base and inference engine. Expose forward and backward reasoning, matching, indexing, and conflict resolution, with Prolog as interpreter.

Explore knowledge representation with semantic nets and frames, detailing instance, subset, and has part relations, inference via intersection search and inheritance, and frames with attributes and slot fillers.

Explore uncertain knowledge and reasoning, introducing probabilistic reasoning and Bayes rule to represent uncertainty, update beliefs with prior and posterior probabilities, and bayesian networks.

Master the planning problem and means-ends analysis. Examine state, goal, and action representations, see a simple planning agent solve the blocks world with strips using unstack, stack, lift off, place.

Explore how machine learning differs from traditional programming, learn core definitions and goals, major learning types, and the training and testing workflow with common algorithms and representations.

Learn how machines mimic human learning by using supervised and unsupervised approaches to build predictive models from training data, enabling classification, prediction, and grouping across real world problems.

Explore the fundamentals of machine learning, including generative, discriminative, and imitative paradigms, plus supervised, unsupervised, semi-supervised, and ensemble methods with practical examples.