CS50's Introduction to Artificial Intelligence with Python repo.
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 |
|---|---|
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| Nodes and Frontiers | MinMax Strategy |
| Breadth First Search | Adversarial Search |
| Depth First Search |
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 |
|---|---|
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| Proposition Logic | Knowledge representation |
| Inference |
TODO: Talk about bayes-rule, bayesian network, probability distribution, markov chains, transition model.
| Pagerank | Heredity |
|---|---|
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| 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) |
|---|---|---|
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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 |
|---|---|
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| 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 |
|---|---|
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| Simple Classification Model | Reinforcement Learning |
| Q-Learning |
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 | |
|---|---|
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| Image Classification | |
| Hidden Layers | |
| Convolution | |
| Pooling Layers | |
| Dropout |
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 |
|---|---|
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| Context Free Grammar | Term Frequency |
| Inverse Document Frequency | |
| Markov Chains | |
| Word Embeddings |