Course

Artificial intelligence with Python by CS50

How do you rate this course?

1

2

3

4

5

Teacher CS50

This is CS50, Harvard University's introduction to the intellectual enterprises of computer science and the art of programming. Demanding, but definitely doable. Social, but educational. A focused topic, but broadly applicable skills. CS50 is the quintessential Harvard (and Yale!) course.

Share

Course content

0h01m

Introduction - CS50's Introduction to Artificial Intelligence with Python 2020

***

This is CS50, Harvard University's introduction to the intellectual enterprises of computer science and the art of programming.

***

HOW TO SUBSCRIBE

http://www.youtube.com/subscription_center?add_user=cs50tv

HOW TO TAKE CS50

edX: https://cs50.edx.org/
Harvard Extension School: https://cs50.harvard.edu/extension
Harvard Summer School: https://cs50.harvard.edu/summer
OpenCourseWare: https://cs50.harvard.edu/x

HOW TO JOIN CS50 COMMUNITIES

Discord: https://discord.gg/T8QZqRx
Ed: https://cs50.harvard.edu/x/ed
Facebook Group: https://www.facebook.com/groups/cs50/
Faceboook Page: https://www.facebook.com/cs50/
GitHub: https://github.com/cs50
Gitter: https://gitter.im/cs50/x
Instagram: https://instagram.com/cs50
LinkedIn Group: https://www.linkedin.com/groups/7437240/
LinkedIn Page: https://www.linkedin.com/school/cs50/
Quora: https://www.quora.com/topic/CS50
Slack: https://cs50.edx.org/slack
Snapchat: https://www.snapchat.com/add/cs50
Twitter: https:

1h49m

Search - Lecture 0 - CS50's Introduction to Artificial Intelligence with Python 2020

00:00:00 - Introduction
00:00:15 - Artificial Intelligence
00:03:14 - Search
00:14:17 - Solving Search Problems
00:25:57 - Depth First Search
00:28:30 - Breadth First Search
00:54:29 - Greedy Best-First Search
01:05:15 - A* Search
01:12:01 - Adversarial Search
01:14:09 - Minimax
01:36:17 - Alpha-Beta Pruning
01:45:28 - Depth-Limited Minimax

This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course's end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enabl

1h47m

Knowledge - Lecture 1 - CS50's Introduction to Artificial Intelligence with Python 2020

00:00:00 - Introduction
00:00:15 - Knowledge
00:04:52 - Propositional Logic
00:21:47 - Inference
00:40:06 - Knowledge Engineering
01:04:33 - Inference Rules
01:30:31 - Resolution
01:38:25 - First-Order Logic

This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course's end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

https://www.youtube.com/playlist?list=PLhQjrBD2T382Nz7z1AEXmioc27axa19Kv

***

This

1h54m

Uncertainty - Lecture 2 - CS50's Introduction to Artificial Intelligence with Python 2020

00:00:00 - Introduction
00:00:15 - Uncertainty
00:04:52 - Probability
00:09:37 - Conditional Probability
00:17:19 - Random Variables
00:26:28 - Bayes' Rule
00:34:01 - Joint Probability
00:40:13 - Probability Rules
00:49:42 - Bayesian Networks
01:21:00 - Sampling
01:32:58 - Markov Models
01:44:17 - Hidden Markov Models

This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course's end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intell

1h44m

Optimization - Lecture 3 - CS50's Introduction to Artificial Intelligence with Python 2020

00:00:00 - Introduction
00:00:15 - Optimization
00:01:20 - Local Search
00:07:24 - Hill Climbing
00:29:43 - Simulated Annealing
00:40:43 - Linear Programming
00:51:03 - Constraint Satisfaction
00:59:17 - Node Consistency
01:03:03 - Arc Consistency
01:16:53 - Backtracking Search

This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course's end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

https://www.

1h45m

Learning - Lecture 4 - CS50's Introduction to Artificial Intelligence with Python 2020

00:00:00 - Introduction
00:00:15 - Machine Learning
00:01:15 - Supervised Learning
00:08:11 - Nearest-Neighbor Classification
00:12:30 - Perceptron Learning
00:33:19 - Support Vector Machines
00:39:31 - Regression
00:42:37 - Loss Functions
00:49:33 - Overfitting
00:55:44 - Regularization
00:59:42 - scikit-learn
01:09:57 - Reinforcement Learning
01:13:02 - Markov Decision Processes
01:19:56 - Q-learning
01:38:54 - Unsupervised Learning
01:40:19 - k-means Clustering

This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course's end, students

1h41m

Neural Networks - Lecture 5 - CS50's Introduction to Artificial Intelligence with Python 2020

00:00:00 - Introduction
00:00:15 - Neural Networks
00:05:41 - Activation Functions
00:07:47 - Neural Network Structure
00:16:02 - Gradient Descent
00:30:00 - Multilayer Neural Networks
00:32:58 - Backpropagation
00:36:27 - Overfitting
00:38:52 - TensorFlow
00:53:01 - Computer Vision
00:58:09 - Image Convolution
01:08:18 - Convolutional Neural Networks
01:27:03 - Recurrent Neural Networks

This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course's end, students emerge with experience in libraries for machine learning as well as knowledge

1h54m

Language - Lecture 6 - CS50's Introduction to Artificial Intelligence with Python 2020

00:00:00 - Introduction
00:00:15 - Language
00:04:55 - Syntax and Semantics
00:10:23 - Context-Free Grammar
00:20:35 - nltk
00:28:00 - n-grams
00:30:28 - Tokenization
00:38:00 - Markov Models
00:42:41 - Bag-of-Words Model
00:46:38 - Naive Bayes
01:09:18 - Information Retrieval
01:12:06 - tf-idf
01:21:04 - Information Extraction
01:30:13 - WordNet
01:32:06 - Word Representation
01:38:18 - word2vec

This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course's end, students emerge with experience in libraries for machine learning as well as k