K-means 1 | IREA

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Artificial Intelligence

Artificial Intelligence for young people – AIM4YOU

Welcome to AIM4YOU 😊 - course trailer (1:00)
About this course
Promo video 2022 (6:14)

A gentle introduction

Prelude
Getting started
First thoughts (1:42)
Examples 1 (1:24)
Examples 2 (1:02)
Examples 3 (9:02)
Examples 4 (4:24)
AI hype (5:48)
AI definition (7:47)
The Turing test (8:42)
AI features and practice 1 (3:48)
AI features and practice 2 (5:49)
AI features and practice 3 (6:44)
To AI or not to AI (7:58)
Further readings

Core AI: representation and reasoning

Prelude
Cognitive psychology 1 (5:30)
Cognitive psychology 2 (5:12)
Cognitive psychology 3 (3:59)
Types of knowledge (2:32)
Search (5:18)
Intelligent agents (4:56)
Representation and reasoning 1 (5:05)
Representation and reasoning 2 (5:26)
Representation and reasoning 3 (1:51)
Quiz
Further readings

Machine learning

Prelude
Introduction to machine learning (5:40)
Matrices and vectors 1 (4:53)
Matrices and vectors 2 (4:12)
Eigenvalues and eigenvectors (6:52)
Principal component analysis (7:51)
Machine learning as a prediction engine (1:41)
Common learning mechanisms (2:15)
Introduction to supervised machine learning algorithms (2:51)
Linear regression (2:41)
Naive Bayes (2:40)
Support vector machines (2:13)
K-nearest neighbors (1:50)
Decision tree (3:57)
Random forest (1:37)
Introduction to unsupervised machine learning algorithms (2:06)
K-means 1 (4:46)
K-means 2 (2:20)
DB-scan (6:19)
Hierarchical clustering (1:04)
Quiz
Further readings

Neural networks

Prelude
Neuron as a function (2:29)
Types of neural networks (1:27)
Single-layer perceptron (2:08)
Multi-layer perceptron (1:36)
Radial basis function (2:01)
Self-organizing map (3:19)
Extreme learning machine (2:09)
Loss functions (1:33)
Learning algorithms (3:33)
Gradient descent (4:03)
Problems with SGD and their solutions (5:40)
Problems during construction and training of neural networks (3:54)
Vanishing and exploding gradients (6:01)
Overfitting and underfitting (4:19)
Convolutional neural networks (5:29)
Various CNN architectures (4:34)
Recurrent neural networks (6:52)
Long short-term memory (4:29)
Autoencoders (4:55)
Generative adversarial network (5:42)
Quiz
Further readings

Natural language processing

Prelude
Introduction to NLP (5:25)
NLP foundations (11:38)
Language modelling (5:35)
Parsing (8:25)
Semantics (4:27)
Word2vec (4:21)
Contextual word embedding (4:37)
Classification tasks in NLP (6:42)
Sentiment analysis (5:42)
Text/document clustering with K-means (6:22)
Topic modeling 1 (3:58)
Topic modeling 2 (3:14)
Topic modeling 3 (4:30)
Quiz
Further readings

Real-world applications

Prelude
AI home assistants (4:36)
Self-driving cars (4:41)
Black lives matter (4:47)
Robotics 1 (5:02)
Robotics 2 (1:51)
Robotics 3 (2:39)
Robotics 4 (1:45)
Robotics 5 (1:00)
Solar concentrator (1:22)
Further readings

Review. Ethics and personalization in AI

Prelude
Review 1 (7:31)
Review 2 (4:07)
Personalization 1 (5:14)
Personalization 2 (5:23)
Ethics 1 (2:27)
Ethics 2 (3:51)
Quiz
Further readings

K-means 1

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