Lecture Artificial Intelligence

Lecture Artificial intelligence: AlexNet. This lecture provides students with content including: winner of ImageNet LSVRC-2012; image classification problem; 5 convolutional layers; 3 fully-connected layers; training on Multiple GPUs; overlapping pooling;... Please refer to the detailed content of the lecture!

20p codabach1016 03-05-2024 0 0   Download

Lecture Artificial intelligence: Artificial neural network. This lecture provides students with content including: computing systems inspired by biological neural networks; consists of several processing elements that receive inputs and deliver outputs; "learn" to perform tasks by considering examples; be able to model nonlinear processes;... Please refer to the detailed content of the lecture!

47p codabach1016 03-05-2024 2 0   Download

Lecture Artificial intelligence: Binary classifiers for multi-class classification problems. This lecture provides students with content including: classification; binary classification; multi-class classification; binary classifiers; support vector machines; perceptron; logistic regression;... Please refer to the detailed content of the lecture!

12p codabach1016 03-05-2024 3 0   Download

Lecture Artificial intelligence: k-means clustering. This lecture provides students with content including: group together similar instances high intra-cluster similarity and low inter-cluster similarity; document clustering; customer segmentation; recommendation engines; image compression;... Please refer to the detailed content of the lecture!

20p codabach1016 03-05-2024 2 0   Download

Lecture Artificial intelligence: k-Nearest Neighbors. This lecture provides students with content including: the kNN requires; three-class 2D problem; Euclidian distance; feature nomalization; feature weighting; computational complexity; kNN - a lazy learning algorithm;... Please refer to the detailed content of the lecture!

21p codabach1016 03-05-2024 1 0   Download

Lecture Artificial intelligence: Linear regression. This lecture provides students with content including: what is regression analysis; simple linear regression; least-square linear regression problem; multiple linear regression; loss function;... Please refer to the detailed content of the lecture!

22p codabach1016 03-05-2024 0 0   Download

Lecture Artificial intelligence: Long short term memory networks. This lecture provides students with content including: recurrent neural network; long short term memory; vanishing gradient; exploding gradient; suffering from long-term dependency;... Please refer to the detailed content of the lecture!

14p codabach1016 03-05-2024 1 0   Download

Lecture Artificial intelligence: Perceptron learning algorithm. This lecture provides students with content including: dot product of 2 vectors; angle between 2 vectors; single perceptron;... Please refer to the detailed content of the lecture!

11p codabach1016 03-05-2024 6 0   Download

Lecture Artificial intelligence: Q learning. This lecture provides students with content including: supervised learning; unsupervised learning; reinforcement learning; utilize the Q matrix;... Please refer to the detailed content of the lecture!

18p codabach1016 03-05-2024 1 0   Download

Lecture Artificial intelligence: Recurrent neural network. This lecture provides students with content including: feedforward neural network; recurrent neural network; language model - character level;... Please refer to the detailed content of the lecture!

16p codabach1016 03-05-2024 1 0   Download

Lecture Artificial intelligence: Soft margin support vector machine. This lecture provides students with content including: support vector machine; soft margin SVM;... Please refer to the detailed content of the lecture!

7p codabach1016 03-05-2024 2 0   Download

Lecture Artificial intelligence: Softmax function. This lecture provides students with content including: usual output layer for classification tasks; ordinary classifier; probabilistic classifier;... Please refer to the detailed content of the lecture!

8p codabach1016 03-05-2024 1 0   Download

Lecture Artificial intelligence: Support vector machine. This lecture provides students with content including: distance from a point to a line/plane/hyperplane; equal margins; largest margins; optimization problem;... Please refer to the detailed content of the lecture!

7p codabach1016 03-05-2024 0 0   Download

Lecture Artificial intelligence: Uncertainty. This lecture provides students with content including: probability - brief review; robot localization problem; Bayesian filter; particle filter;... Please refer to the detailed content of the lecture!

26p codabach1016 03-05-2024 1 0   Download

Artificial intelligence - Lecture 1: Introduction. This lecture provides students with content including: what is AI; foundations of AI; short history of AI; philosophical discussions;... Please refer to the detailed content of the lecture!

4p codabach1016 03-05-2024 4 2   Download

Artificial intelligence - Lecture 2: Agent. This lecture provides students with content including: agents and environments; PEAS (Performance measure, Environment, Actuators, Sensors); environment types; agent types;... Please refer to the detailed content of the lecture!

6p codabach1016 03-05-2024 7 2   Download

Artificial intelligence - Lecture 3: Search. This lecture provides students with content including: problem-solving agents; problem types; problem formulation; example problems; basic search algorithms; breadth-first search; depth-first search; depth-limited search; iterative deepening depth-first search;... Please refer to the detailed content of the lecture!

9p codabach1016 03-05-2024 3 2   Download

Artificial intelligence - Lecture 4: Search. This lecture provides students with content including: graph search; best-first search; A* search; straight line distances; greedy best-first search; admissible heuristics;... Please refer to the detailed content of the lecture!

10p codabach1016 03-05-2024 3 2   Download

Artificial intelligence - Lecture 6: Advanced search methods. This lecture provides students with content including: local beam search; game and search; alpha-beta pruning; relation of games to search; optimal strategies; minimax algorithm;... Please refer to the detailed content of the lecture!

10p codabach1016 03-05-2024 4 2   Download

Artificial intelligence - Lecture 9: Propositional logic. This lecture provides students with content including: knowledge-based agents; propositional logic; language of propositional logic; formal language of propositional logic; semantic of propositional logic; inference in propositional logic; forward chaining;... Please refer to the detailed content of the lecture!

13p codabach1016 03-05-2024 3 2   Download