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teaching:ws2018:ml4cv [2019/02/07 20:07] chiotell |
teaching:ws2018:ml4cv [2019/04/18 11:20] chiotell |
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==Prerequisites== | ==Prerequisites== |
Linear Algebra, Calculus and Probability Theory are essential builex3_pgms.pdfding blocks to this course. The homework exercises do not have to be handed in. Solutions for the programming exercises will be provided in ** Python **. | Linear Algebra, Calculus and Probability Theory are essential building blocks to this course. The homework exercises do not have to be handed in. Solutions for the programming exercises will be provided in ** Python **. |
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==Exercises== | ==Exercises== |
0. {{teaching:ws2018:ml4cv:ex0_linalg.pdf | Linear Algebra}} | 0. {{teaching:ws2018:ml4cv:ex0_linalg.pdf | Linear Algebra}} |
{{teaching:ws2018:ml4cv:ex0_sol_linalg.pdf | Solutions}} | |
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1. {{teaching:ws2018:ml4cv:ex1_probs.pdf | Probabilistic Reasoning}} {{teaching:ws2018:ml4cv:ex1_sol_probs.zip | Solutions}} | 1. {{teaching:ws2018:ml4cv:ex1_probs.pdf | Probabilistic Reasoning}} |
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2. {{teaching:ws2018:ml4cv:ex2_regression.pdf | Regression}} | 2. {{teaching:ws2018:ml4cv:ex2_regression.pdf | Regression}} |
{{teaching:ws2018:ml4cv:ex2_sol_regression.pdf | Solutions}} | |
{{teaching:ws2018:ml4cv:polynomial_regression.zip | Code}} | |
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3. {{teaching:ws2018:ml4cv:ex3_pgms.pdf | Graphical Models}} | 3. {{teaching:ws2018:ml4cv:ex3_pgms.pdf | Graphical Models}} |
{{teaching:ws2018:ml4cv:graph.py.zip | graph.py}} | {{teaching:ws2018:ml4cv:graph.py.zip | graph.py}} |
{{teaching:ws2018:ml4cv:ex3_sol_pgms.pdf | Solutions}} | |
{{teaching:ws2018:ml4cv:graph_solution.zip | Code}} | |
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4. {{teaching:ws2018:ml4cv:ex4_bagging_boosting.pdf | Bagging and Boosting}} | 4. {{teaching:ws2018:ml4cv:ex4_bagging_boosting.pdf | Bagging and Boosting}} |
{{teaching:ws2018:ml4cv:banknote_auth.zip | banknote_auth.zip}} | {{teaching:ws2018:ml4cv:banknote_auth.zip | banknote_auth.zip}} |
{{teaching:ws2018:ml4cv:ex4_sol_bagging_boosting.pdf | Solutions}} | |
{{teaching:ws2018:ml4cv:adaboost.zip | Code}} | |
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5. {{teaching:ws2018:ml4cv:ex5_metric_learning.pdf | Metric Learning}} | 5. {{teaching:ws2018:ml4cv:ex5_metric_learning.pdf | Metric Learning}} |
{{teaching:ws2018:ml4cv:ex5_sol_metric_learning.pdf | Solutions}} | |
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6. {{teaching:ws2018:ml4cv:ex6_kernels_gps.pdf | Kernels and Gaussian Processes}} | 6. {{teaching:ws2018:ml4cv:ex6_kernels_gps.pdf | Kernels and Gaussian Processes}} |
{{teaching:ws2018:ml4cv:ex7_sol_kernels_gps.pdf | Solutions}} | |
{{teaching:ws2018:ml4cv:gp_regression.zip | Code}} | |
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7. {{teaching:ws2018:ml4cv:ex7_deeplearning.pdf | Deep Learning}} | 7. {{teaching:ws2018:ml4cv:ex7_deeplearning.pdf | Deep Learning}} |
{{teaching:ws2018:ml4cv:ex7_sol_deepnets.pdf | Solutions}} | |
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8. {{teaching:ws2018:ml4cv:ex8_clustering1.pdf | Clustering}} | 8. {{teaching:ws2018:ml4cv:ex8_clustering1.pdf | Clustering}} |
{{teaching:ws2018:ml4cv:ex8_sol_clustering.pdf | Solutions}} | |
{{teaching:ws2018:ml4cv:clustering_sol.zip | Code}} | |
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9. {{teaching:ws2018:ml4cv:ex9_vi.pdf | Variational Inference |}} | 9. {{teaching:ws2018:ml4cv:ex9_vi.pdf | Variational Inference |}} |
{{teaching:ws2018:ml4cv:ex9_sol_vi1.pdf | Solutions}} | |
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10. {{teaching:ws2018:ml4cv:ex10_vi2.pdf | Variational Inference ||}} | 10. {{teaching:ws2018:ml4cv:ex10_vi2.pdf | Variational Inference ||}} |
{{teaching:ws2018:ml4cv:ex10_sol_vi2.pdf | Solutions}} | |
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11. {{teaching:ws2018:ml4cv:ex11_sampling.pdf | Sampling}} | 11. {{teaching:ws2018:ml4cv:ex11_sampling.pdf | Sampling}} |
{{teaching:ws2018:ml4cv:ex11_sol_sampling.pdf | Solutions}} | |
{{teaching:ws2018:ml4cv:particle_filter.zip | Code}} | |
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