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Ethics of Artificial Intelligence - Part 1 :: Machine Intelligence Course, Lecture 23

SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo) Target Audience: Senior Undergraduate Engineering Students Instructor: Professor H.R.Tizhoosh (http://kimia.uwaterloo.ca/) Course Outline - The objective of this course is to introduce the students to the main concepts of machine intelligence as parts of a broader framework of “artificial intelligence”. An overview of different learning, inference and optimization schemes will be provided, including Principal Component Analysis, Support Vector Machines, Self-Organizing Maps, Decision Trees, Backpropagation Networks, Autoencoders, Convolutional Networks, Fuzzy Inferencing, Bayesian Inferencing, Evolutionary algorithms, and Ant Colonies. Lecture 1 – Introduction (Definition of Intelligence, terminology, history of AI, Turing Test, Chinese Room) Lecture 2 – Principal Components Analysis (PCA) Lecture 3 – Linear Discriminant Analysis (LDA), t-distributed Stochastic Neighbor Embeddings (t-SNE) Lecture 4 – AI and Vision, feature extraction, Harris corners, Fisher Vector, VLAD, SIFT, Bag of Visual Words Lecture 5 – AI and data, generalization and memorization, K-fold cross-validation, leave-one-out, regularization, overfitting, underfitting Lecture 6 – Clustering, K-means, self-organizing maps (SOM) Lecture 7 – Classification, support vector machines (SVM) Lecture 8 – Cluster validity, SSW, SSB, Dunn’s Index, WB Index, Fuzzy sets and fuzzy c-means (FCM) Lecture 9 – Linear regression, artificial neurons, abstractions of neurons, weight adjustment for plasticity Lecture 10 – Artificial Neural Networks, XOR problem, hidden layers, learning algorithm, multi-layer perceptrons (MLPs), Delta Rule Lecture 11 – Backpropagation networks (incremental and batch-wise), stopping criteria, autoencoders Lecture 12 – Restricted Boltzmann Machines (RBMs), training deep autoencoders Lecture 13 – Neocognitron, Convolutional Neural Networks (CNNs), overfitting in deep learning Lecture 14 – Reinforcement Learning, reward and punishment Lecture 15 – Designing Reinforcement Learning agents, Temporal differencing, Q-learning Lecture 16 – Decision Trees, entropy, information gain Lecture 17 – Fuzzy Logic, modus ponens, modus tollens, inference, fuzzy control, inverted pendulum Lecture 18 – Bayesian Learning, Bayes Theorem, probability rules Lecture 19 - Naïve Bayes classifier Lecture 20 – Evolutionary algorithms, genetic algorithms Lecture 21 – Genetic algorithms: encoding, crossover and mutation, different models, differential evolution, opposition-based learning Lecture 22 – Swarm Intelligence, Ant Colony Optimization (ACO) Lecture 23 – Ethics of Artificial Intelligence, Part 1 (Philosophy) Lecture 24 – Ethics of Artificial Intelligence, Part 2 (Practical Cases)