Welcome to my journey through CS50’s Introduction to AI with Python! This course takes a deep dive into the concepts and algorithms that form the foundation of modern artificial intelligence.
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Certificate of Completion: Edx, Harvardx CS50
Throughout the lectures, I've taken notes on key concepts and algorithms that was covered. Here's a glimpse of what I've learned:
- Agent: An entity that perceives its environment and takes action based on that perception.
- State: A configuration of the agent and its environment.
- Actions: Choices that can be made in a given state.
- Transition model: A description of the state resulting from performing any applicable action.
- Path cost: A numerical cost associated with a specific path.
- Evaluation function: A function that estimates the expected utility of a game from a given state.
- DFS (depth-first search): Explores the deepest node in the frontier first.
- BFS (breadth-first search): Explores the shallowest node in the frontier first.
- Greedy best-first search: Expands the node that is closest to the goal, based on an estimated heuristic function.
- A* search: Expands the node with the lowest value of "cost to reach node" plus "estimated goal cost."
- Minimax: An adversarial search algorithm.
- Sentence: An assertion about the world in a knowledge representation language.
- Knowledge base: A set of sentences known by a knowledge-based agent.
- Entailment: Sentence A entails sentence B if, in every model where sentence A is true, sentence B is also true.
- Inference: The process of deriving new sentences from existing ones.
- Conjunctive normal form: A logical sentence that is a conjunction of clauses.
- First-order logic: Propositional logic.
- Second-order logic: Proposition logic with universal and existential quantification.
- Model checking: Enumerating all possible models to check the truth of a proposition.
- Conversion to CNF and Inference by resolution
- Unconditional probability: Degree of belief in a proposition in the absence of any other evidence.
- Conditional probability: Degree of belief in a proposition given some evidence that has already been revealed.
- Random variable: A variable in probability theory with a domain of possible values.
- Independence: The knowledge that one event occurs does not affect the probability of the other event.
- Bayes' Rule: P(a) P(b| a) = P(b) P(a|b) |
- Bayesian network: A data structure that represents the dependencies among random variables.
- Markov assumption: The assumption that the current state depends on only a finite fixed number of previous states.
- Markov chain: A sequence of random variables where the distribution of each variable follows the Markov assumption.
- Hidden Markov Model: A Markov model for a system with hidden states that generate some observed events.
- Inference by enumeration
- Sampling
- Likelihood weighting
- Optimization: The process of choosing the best option from a set of options.
- Local Search Hill Climbing
- Steepest-ascent: Choose the highest-valued neighbor.
- Stochastic: Choose randomly from higher-valued neighbors.
- First-choice: Choose the first higher-valued neighbor.
- Random-restart: Conduct hill climbing multiple times.
- Local beam search: Choose the k highest-valued neighbors.
- Simulated Annealing: Accept worse-valued neighbors early on to explore different solutions.
- Linear Programming
- Simplex
- Interior-Point
- Constraint Satisfaction Problems
- Arc consistency: Make X arc-consistent with respect to Y by removing elements from X's domain until every choice for X has a possible choice for Y.
- Backtracking search
- Supervised Learning: Learning a function to map inputs to outputs using a data set of input-output pairs.
- Classification: Learning a function that maps an input point to a discrete category.
- Regression: Learning a function that maps an input point to a continuous value.
- Loss function: Measures how poorly a hypothesis performs.
- Overfitting: When a model fits too closely to a particular data set and fails to generalize.
- Regularization: Penalizing complex hypotheses in favor of simpler ones.
- Holdout Cross-Validation: Splitting data into training and test sets for evaluation.
- k-fold Cross-Validation: Splitting data into k sets for evaluation, using each set as a test set once.
- Reinforcement Learning: Learning what actions to take in the future based on rewards or punishments.
- Unsupervised Learning: Learning patterns from input data without additional feedback.
- Clustering: Organizing objects into groups based on their similarities.
- k-Nearest Neighbor Classification: Choosing the most common class among the k nearest data points to an input.
- Support Vector Machines (SVM)
- Markov Decision Process: A model for decision-making with states, actions, and rewards.
- Q-Learning: Learning a function Q(s, a) that estimates the value of performing action a in state s.
- Greedy Decision-Making
- Epsilon-Greedy
- k-Means Clustering: Clustering data by assigning points to clusters and updating cluster centers.
- Neural Network: A network of interconnected artificial neurons that can learn from data.
- Artificial Neuron/Perceptron: A mathematical function that takes inputs, applies weights and biases, and produces an output.
- Activation Function: A function that determines the output of an artificial neuron.
- Feedforward Neural Network: A neural network where information flows in one direction, from the input layer to the output layer.
- Backpropagation: A learning algorithm for adjusting the weights and biases of a neural network.
- Convolutional Neural Network (CNN): A type of neural network commonly used for image recognition and processing.
- Recurrent Neural Network (RNN): A type of neural network that can process sequential data by using feedback connections.
- Long Short-Term Memory (LSTM): A type of RNN that can learn long-term dependencies and is effective in handling sequential data.
- Perceptron Learning Algorithm: An algorithm for training a single artificial neuron to classify linearly separable data.
- Gradient Descent: An optimization algorithm that adjusts the weights and biases of a neural network based on the error gradient.
- Stochastic Gradient Descent (SGD): A variant of gradient descent that randomly selects a subset of training examples (mini-batch) for each iteration.
- Convolution: An operation that applies a filter/kernel to an input image to extract features.
- Pooling: An operation that reduces the spatial size of the input by selecting the maximum or average value within a region.
- Recurrent Neural Network (RNN): A type of neural network that uses recurrent connections to process sequential data.
- Long Short-Term Memory (LSTM): A type of RNN that can learn long-term dependencies by using a gating mechanism to control information flow.
- Language Modeling: Building statistical models of language to predict the probability of a sequence of words.
- n-gram: A sequence of n words or characters used in language modeling.
- Part-of-Speech Tagging: Assigning a grammatical category (noun, verb, adjective, etc.) to each word in a sentence.
- Hidden Markov Models (HMM): Probabilistic models used to model sequential data with hidden states.
- Information Retrieval: Finding relevant information from a collection of documents based on a user's query.
- Term Frequency-Inverse Document Frequency (TF-IDF): A numerical statistic used to reflect the importance of a term in a document within a collection.
- Word Embeddings: Dense vector representations of words that capture semantic relationships.
- Word2Vec: A popular word embedding model that learns word representations based on the context in which they appear.
- n-gram Language Modeling: Estimating the probability of a word given its previous n-1 words.
- Hidden Markov Models (HMM): Modeling sequential data with hidden states and observed emissions.
- Viterbi Algorithm: Finding the most likely sequence of hidden states in an HMM given an observed sequence.
- Term Frequency-Inverse Document Frequency (TF-IDF): Calculating the importance of a term in a document relative to a collection.
- Vector Space Model: Representing documents and queries as vectors in a high-dimensional space for information retrieval.
- Cosine Similarity
