Lecture 0: Search
Introduction to artificial intelligence focusing on search problems, depth-first search, breadth-first search, greedy best-first search, A* search, minimax, and alpha-beta pruning.
#Search#Depth-First Search#Breadth-First Search
cs50 ai
Explore articles on cs50 ai
Lecture 1: Knowledge
Introduction to artificial intelligence focusing on propositional logic, entailment, inference, model checking, resolution, and first order logic.
#Propositional Logic#Entailment#Inference
Lecture 2: Uncertainty
Introduction to artificial intelligence focusing on probability, conditional probability, random variables, independence, Bayes' rule, joint probability, Bayesian networks, sampling, Markov models, and hidden Markov models.
#Probability#Conditional Probability#Random Variables
Lecture 3: Optimization
Introduction to artificial intelligence focusing on local search, hill climbing, simulated annealing, linear programming, constraint satisfaction, and backtracking search.
#Local Search#Hill Climbing#Simulated Annealing
Lecture 4: Learning
Introduction to artificial intelligence focusing on supervised learning, nearest-neighbor classification, perceptron learning, support vector machines, regression, loss functions, overfitting, regularization, reinforcement learning, Markov decision processes, Q-learning, unsupervised learning, and k-means clustering.
#Supervised Learning#Nearest-Neighbor Classification#Perceptron Learning
Lecture 5: Neural Networks
Introduction to artificial intelligence focusing on artificial neural networks, activation functions, gradient descent, backpropagation, overfitting, TensorFlow, image convolution, convolutional neural networks, and recurrent neural networks.
#Artificial Neural Networks#Activation Functions#Gradient Descent
Lecture 6: Language
Introduction to artificial intelligence focusing on syntax, semantics, context-free grammar, nltk, n-grams, bag-of-words model, Naive Bayes, word representation, word2vec, attention, and transformers.
#Syntax#Semantics#Context-Free Grammar