Basic Computer Knowledge
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.
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 enable them to design intelligent systems of their own.
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.
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 intelligent systems of their own.
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.
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 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.
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 of artificial intelligence principles that enable them to design intelligent systems of their own.
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 knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.
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