Harvard CS50’s Artificial Intelligence with Python – Full University Course

Harvard CS50’s Artificial Intelligence with Python – Full University Course

This comprehensive video course, taught by Brian Yu and published by freeCodeCamp.org, provides a complete introduction to the fundamental concepts, algorithms, and techniques in modern artificial intelligence using Python.

Course Overview & Main Modul

es

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| CS50'S AI WITH PYTHON COURSE STRUCTURE |

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| 1. Search | Depth-First Search, Breadth-First Search, |

| | A* Se

arch, Minimax, Alpha-Beta Pruning |

+---------------------+-------------------------------------------------+

| 2. Knowledge | Propositional Logic, Inference Rules, |

| | Knowledge Engineering, Model Checking |

+---------------------+----------------------------------------

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| 3. Uncertainty | Probability, Bayesian Networks, |

| | Markov Models, Hidden Markov Models |

+---------------------+-------------------------------------------------+

| 4. Optimization | Hill-Climbing, Simulated Annealing, |

|

| Linear Programming, Constraint Satisfaction |

+---------------------+-------------------------------------------------+

| 5. Machine Learning| Supervised Learning, Nearest-Neighbor, |

| | Support Vector Machines, Reinforcement Learning |

+---------------------+------------------------------

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| 6. Neural Networks | Perceptrons, Multi-Layer Networks, CNNs, |

| | Recurrent Neural Networks (RNNs) |

+---------------------+-------------------------------------------------+

| 7. Language | Natural Language Processing (NLP), Syntax, |

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| Markov Models, Word Embeddings, Transformers |

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Detailed Content Breakdown

1. Search Algorithms [02:44]

Problem-Solving Framework: Defines states, actions, transition models, path cost, and goal tests.

Uninformed Search:

Depth-First Search

(DFS): Uses a Stack (LIFO) to explore deep paths first.

Breadth-First Search (BFS): Uses a Queue (FIFO) to explore nodes level-by-level.

Informed Search:

Greedy Best-First Search: Evaluates nodes based on heuristic function h(n).

A Search:* Combines path cost g(n) and heuristic estimate h(n) using f(n)=g(n)+h(n).

Adversarial

Search:

Minimax: Optimizes decision-making in two-player zero-sum games (e.g., Tic-Tac-Toe, Chess).

Alpha-Beta Pruning: Optimizes Minimax search by eliminating non-influential subtrees.

2. Knowledge & Inference [03:45]

Knowledge Representation: Representing information using formal logic symbols and rules.

Propositional Logic:

Truth values, logical connectives (∧, ∨, ¬, ⟹, ⟺).

Inference Engines: Deriving new conclusions from existing facts (Model Checking, Resolution).

3. Uncertainty & Probabilistic Reasoning [04:03]

Handling Incomplete Information: Utilizing probability theory when facts are uncertain.

Bayesian Networks: Directed acyclic graphs re

presenting conditional independence among random variables.

Markov Models & Hidden Markov Models (HMMs): Modeling sequential data over time (e.g., weather patterns, speech tracking).

4. Optimization Problems [04:19]

Local Search Techniques:

Hill-Climbing: Moving toward neighboring states with higher evaluation scores.

Simulate

d Annealing: Temperature-controlled state selection to escape local optima.

Constraint Satisfaction Problems (CSPs): Assigning variables values while satisfying specific constraints (e.g., Map Coloring, Sudoku).

5. Machine Learning & Reinforcement Learning [04:37]

Supervised Learning: Training models using labeled data.

Classi

fication algorithms (k-Nearest Neighbors, Naive Bayes, Support Vector Machines).

Reinforcement Learning: Agents learning optimal behavior through rewards and penalties (e.g., Q-Learning).

6. Neural Networks & Deep Learning [05:05]

Artificial Neural Networks: Modeling biological neurons with weights, biases, and activation func

tions.

Architectures:

Convolutional Neural Networks (CNNs): Specialized for image classification and feature extraction.

Recurrent Neural Networks (RNNs): Tailored for processing sequential input data.

7. Natural Language Processing (NLP) [05:21]

Syntactic & Semantic Analysis: Parsing sentences, tokenization, context-free gram

mars.

Word Embeddings: Representing word meanings in dense vector spaces (e.g., Word2Vec).

Modern Architectures: Attention mechanisms and Transformer models [11:48:10].

Video Source

URL: http://www.youtube.com/watch?v=5NgNicANyqM

Harvard CS50’s Artificial Intelligence with Python – Full University Course

freeCodeCamp.org · 4.8