Topics: Problem-solving as search
Uninformed search algorithms (DFS, BFS)
Heuristics and informed search (A*, greedy search)

Assignments: Implement a Tic-Tac-Toe solver.
Build a pathfinding AI using A*.

• Topics:Knowledge representation (propositional and predicate logic)
  Logical reasoning and inference
  Constraint satisfaction problems (CSPs)
  
• Assignments:Develop an AI for Minesweeper using logical reasoning.

Topics: Probability and Bayes' rule
Conditional independence
Bayesian networks

Assignments: Create a medical diagnosis AI using probabilistic reasoning.

Topics: Local search and optimization algorithms Hill climbing, simulated annealing Genetic algorithms

Assignments: Solve an optimization problem using a genetic algorithm.

Topics: Supervised learning Classification algorithms (K-Nearest Neighbors, Naive Bayes) Regression and evaluation metrics

Assignments: Train a handwriting recognition system using supervised learning.

Topics: Neural network basics Forward and backward propagation Deep learning with libraries like TensorFlow or PyTorch

Assignments: Build a neural network to classify images (e.g., digits from the MNIST dataset).

Topics: Text tokenization and preprocessing Sentiment analysis Language modeling and sequence prediction

Assignments: Create a chatbot using natural language processing techniques.