Lectures Machine Intelligence – Lecture 1 (methods, history, …) Machine Intelligence – Lecture 2 (Turing Test,…) Machine Intelligence – Lecture 3 (PCA, AI and Data) Machine Intelligence – Lecture 4 (LDA, t-SNE) Machine Intelligence – Lecture 5 (Computer Vision, …) Machine Intelligence – Lecture 6 (Validation, Overfitting, …) Machine Intelligence – Lecture 7 (Clustering, k-means, SOM) Machine Intelligence – Lecture 8 (SOM learning, …) Machine Intelligence – Lecture 9 (Cluster Validity, Probability, Fuzzy Sets, FCM) Machine Intelligence – Lecture 10 (Regression, Neurons, Perceptron, Learning) Machine Intelligence – Lecture 11 (Backpropagation, Topology, Overfitting, …) Machine Intelligence – Lecture 12 (Problems of Learning, RBMs, Autoencoders) Machine Intelligence – Lecture 13 (Convolutional Neural Networks, CNNs) Machine Intelligence – Lecture 14 (Overfitting in Deep Learning, Reinforcement …) Machine Intelligence – Lecture 15 (Reinforcement Learning, Q-Learning) Machine Intelligence – Lecture 16 (Decision Trees) Machine Intelligence – Lecture 17 (Fuzzy Logic,…) Machine Intelligence – Lecture 18 (Evolutionary…) Machine Intelligence – Lecture 19 (Opposition-Based Learning, …) Machine Intelligence – Lecture 20 (Bayesian Learning, Bayes …) Machine Intelligence – Lecture 21 (Naive Bayes, Swarm Intelligence, Ant…) Ethics of Artificial Intelligence – Part 1 :: Machine Intelligence Course, Lecture 23 Ethics of Artificial Intelligence – Part 2 :: Machine Intelligence Course, Lecture 24
