CS50's Introduction to Artificial Intelligence with Python repository.

Search problems can be modeled as an agent within an environment. This agent has an initial state (S), and after fulfilling an action (A), ends up in a resulting new state (resembling a transition model). The objective is to go from an initial state to an end state after a series of actions. As we'll realize, several problems can be thought of as a search problem as well.

TODO: cite frontier and node as main data structures that help understand these solutions.

Degrees	Tic-Tac-Toe
Nodes and Frontiers	MinMax Strategy
Breadth First Search	Adversarial Search
Depth First Search	Alpha-Beta Pruning

TODO: Talk about knowledge representation, how proposition logic is important (and we usually ignore, don't see applications and so on) and how inference is interesting and powerful.

TODO: Talk about resemblance with search problems: given state and action leading to an inferred new proposition.

Knights and Knaves	Minesweeper
Proposition Logic	Knowledge representation
	Inference

TODO: Talk about bayes-rule, bayesian network, probability distribution, markov chains, transition model.

Pagerank	Heredity
Random Surfer Model	Bayesian Network
Markov Chain	Probability Distribution

TODO: Talk about the different types of optimization strategies.

TODO: Talk about concepts similar to previous learnings, frontiers (available worlds), neighbors, resemblance with search and knowledge engineering and representation.

Local Search (Hill-Climbing)	Linear Programming (Lagrange Multipliers)	Constraint Satisfaction Problem (CSP)

TODO: Specify that sudoku was an independent project. Deepen on Constraint Satisfaction Problems and how modeling a sudoku was the tough part. Node Consistency was easy, Arc consistency was tough.

Crossword	Sudoku
Node Consistency	Modeling (Variable, Row, Column, Box)
Arc Consistency	Restriction Representation
AC3 Algorithm	Backtracking

TODO: Talk about the simplicity of shopping. That it should not be considered as a real-life example of what a classification problem look like - explain why (load-data, missing, data pipeline, so on)

TODO: discussion on reinforcement-learning problem. Talk about the interesting (philosophically speaking) part concerning the fact that a learning step, so to speak, is composed at the same time by a reward AND a punishment, because the AI both wins and lose at the same time.

Shopping	Nim
Nearest-Neighbor Classification	Reinforcement Learning
Perceptron Learning	Q-Learning
Support Vector Machines
Loss Functions
scikit-learn

TODO: Talk about neural networks architectures. Image convolution and max-pooling strategies. Dropout for regularization, how it may avoid over-reliance on specific weights.

Traffic
Activation Functions
Gradient Descent
Multilayer Neural Networks
Backpropagation
TensorFlow
Convolutional Neural Networks
Recurrent Neural Networks

TODO: Talk about language structures, context-free grammar, applications on classification and topic modeling (with a nice simple application using only tf-idf for it (not complex LDA or stuff like that)), word representation as basis of a high-dimensional field, distributed representation and how word2vec represents words that appear in the same context more often as closer vectors in this distriuted dimension.

Parser	Questions
Context Free Grammar	Term Frequency
	Inverse Document Frequency
	Markov Chains
	Word Embeddings
	word2vec
	Transformers