The lecture, Institute for Web Science and Technologies, University of Koblenz-Landau, Germany, Dr. Zeyd Boukhers, Dr. Thomas Gottron, Dr. Florian Lemmerich, Dr. Christoph Kling, Prof. Dr. Steffen Staab, 2019, 33 p. K-means. Expectation maximization. DBSCAN. Agglomerative hierarhial clustering.
The lecture, Institute for Web Science and Technologies, University of Koblenz-Landau, Germany, 2019, 50 p. - Defining task - Designing features - Preprocessing -- Outlier removal -- Feature scaling -- Feature correlation measurement -- Missing data - Class imbalance problem
The lecture, Institute for Web Science and Technologies, University of Koblenz-Landau, Germany, Dr. Zeyd Boukhers, 2019, 47 p. Context-dependent classification. Markov chain. Hidden Markov model. Recognition. Decoding. Training. Data dimension reduction. Principal component analysis. Singular value decomposition.
The lecture, Institute for Web Science and Technologies, University of Koblenz-Landau, Germany, Dr. Zeyd Boukhers, 2019, 72 p. Data dimension reduction. Principal component analysis. Singular value decomposition. Clustering. Unsupervised learning. Evaluate clusters. Intrinsic and extrinsic evaluation measures. How K-mean works. Choosing K. EM algorithm.
The lecture, Institute for Web Science and Technologies, University of Koblenz-Landau, Germany, Dr. Zeyd Boukhers, 2019, 47 p. Defining task. Designing features. Preprocessing. Outlier removal. Feature scaling. Feature correlation measurement. Missing data. Class imbalance problem. k-nearest. Classification. Regression. K-D tree. Overfitting. Evaluation. Confusion matrix....
The lecture, Institute for Web Science and Technologies, University of Koblenz-Landau, Germany, Dr. Zeyd Boukhers, 2019, 43 p. Class imbalance problem. k-nearest. Classification. Regression. K-D tree. Overfitting. How to choose the best K. Evaluation. Confusion matrix. Precision. Recall. F-Score. Overall accuracy. Bayesian classification. Bayes theorem. Naive Bayes.
The lecture, Institute for Web Science and Technologies, University of Koblenz-Landau, Germany, Dr. Zeyd Boukhers, 2019, 47 p. How to evaluate a classification model. Bayesian classification. Bayes theorem. Maximum Likelihood Estimation. Maximum a Posteriori Probability Estimation. Bayesian classification. Naïve Bayes. Decision tree.
The lecture, Institute for Web Science and Technologies, University of Koblenz-Landau, Germany, Dr. Zeyd Boukhers, 2019, 36 p. Naïve Bayes. Decision tree. More about decision trees. Pruning. Random forest. Underfitting, overfitting, and generalization.
The lecture, Institute for Web Science and Technologies, University of Koblenz-Landau, Germany, Dr. Zeyd Boukhers, 2019, 45 p. Decision tree. Random forest. Linear regression. Least squares function. Optimization. Linear classification. Perceptron classifier. Support vector machines(SVM). Optimization.
The lecture, Institute for Web Science and Technologies, University of Koblenz-Landau, Germany, Dr. Zeyd Boukhers, 2019, 52 p. Linear regression. Perceptron classifier. Support vector machine. Non-separable cases. Non linearly separable cases. Neural network. From perceptron to one layer perceptron. Multi-layer perceptron. Optimization.
The lecture, Institute for Web Science and Technologies, University of Koblenz-Landau, Germany, Dr. Zeyd Boukhers, 2019, 56 p. XOR problem. Two-layer perceptron. How the network learns. Gradient descend. Backpropagation. Activation functions. Sigmoid. Softmax. Linear. Mixture density. ReLu. Loss functions. Binary cross-entropy. Discrete cross-entropy. Gaussian cross-entropy....
The lecture, Institute for Web Science and Technologies, University of Koblenz-Landau, Germany, Dr. Zeyd Boukhers, 2019, 42 p. Deep neural networks. Context-dependent classification. Markov chain. Hidden Markov model. Recognition. Decoding. Training. Viterbi algorithm.
The lecture, University of Würzburg, Germany, Univ.-Prof. Dr. rer. nat. Ingo Scholtes, 2022, 60 p. Generative Models and Statistical Ensembles. G(n,m) random graph model. G(n,p) random graph model. Degree distribution of the random graph. Random graphs with given degrees. Generative models and likelihood. Statistical inference in networks.
Comments