GE461 Spring 2023

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start [2023/04/01 14:57]
ge461 [Week 9 (Apr 3, Apr 5)] 		start [2023/06/10 07:20] (current)
ge461 [Week 17 (May 29, May 31)]
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 ** Attendance** ** Attendance**
-  * Attendance is mandatory. A student who misses **more than 9 hours** will fail the course automatically. +  * <del>Attendance is mandatory. A student who misses **more than 9 hours** will fail the course automatically.</del> 
  
 ** Exam** ** Exam**
-  * TBD+  * The final exam will be held at EB-103 (for lastnames in the range AKSOY-GÜZEY) and EB-104 (for lastnames in the range HAMURCU-YILDIZ) during 18:00-21:00 on June 10, 2023.
  
 ** Projects** ** Projects**
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 Topic Details: Feature reduction, feature selection, high-dimensional data visualization.\\ Topic Details: Feature reduction, feature selection, high-dimensional data visualization.\\
 Slides and Additional Material: {{ :ge461_dimensionality.pdf |Dimensionality slides}}, {{ :knaw_t-sne_talk.pptx |t-SNE slides}}\\ Slides and Additional Material: {{ :ge461_dimensionality.pdf |Dimensionality slides}}, {{ :knaw_t-sne_talk.pptx |t-SNE slides}}\\
-Project/Exercise-Problem-Set/Homework:\\+Project/Exercise-Problem-Set/Homework: [{{ :ge461_project_dimensionality.pdf |Project}} ({{ :digits.zip |data}})]  (due 23:59 on May 7, 2023)\\
 References: [[https://www.mathworks.com/help/stats/dimensionality-reduction.html|Matlab: dimensionality reduction]], [[https://scikit-learn.org/stable/modules/decomposition.html|Scikit-learn: decomposition]], [[https://scikit-learn.org/stable/auto_examples/index.html#decomposition|Scikit-learn: decomposition examples]], [[https://scikit-learn.org/stable/modules/manifold.html|Scikit-learn: manifold learning]], [[https://www.mathworks.com/discovery/data-visualization.html|Matlab: data visualization]],  References: [[https://www.mathworks.com/help/stats/dimensionality-reduction.html|Matlab: dimensionality reduction]], [[https://scikit-learn.org/stable/modules/decomposition.html|Scikit-learn: decomposition]], [[https://scikit-learn.org/stable/auto_examples/index.html#decomposition|Scikit-learn: decomposition examples]], [[https://scikit-learn.org/stable/modules/manifold.html|Scikit-learn: manifold learning]], [[https://www.mathworks.com/discovery/data-visualization.html|Matlab: data visualization]], 
 [[https://matplotlib.org/|Matplotlib: data visualization]], [[https://lvdmaaten.github.io/tsne/|t-SNE]]\\ [[https://matplotlib.org/|Matplotlib: data visualization]], [[https://lvdmaaten.github.io/tsne/|t-SNE]]\\
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 ** Unsupervised learning, clustering.  ** [Aksoy] \\ ** Unsupervised learning, clustering.  ** [Aksoy] \\
 Topic Details: K-means clustering, mixture models, hierarchical clustering.\\ Topic Details: K-means clustering, mixture models, hierarchical clustering.\\
-Slides and Additional Material:\\+Slides and Additional Material: {{ :ge461_clustering.pdf |Clustering slides}}\\
 Project/Exercise-Problem-Set/Homework:  \\ Project/Exercise-Problem-Set/Homework:  \\
 References: [[https://www.mathworks.com/help/stats/cluster-analysis.html|Matlab: cluster analysis]], [[https://scikit-learn.org/stable/modules/clustering.html|Scikit-learn: clustering]], [[https://scikit-learn.org/stable/auto_examples/index.html#clustering|Scikit-learn: clustering examples]]\\ References: [[https://www.mathworks.com/help/stats/cluster-analysis.html|Matlab: cluster analysis]], [[https://scikit-learn.org/stable/modules/clustering.html|Scikit-learn: clustering]], [[https://scikit-learn.org/stable/auto_examples/index.html#clustering|Scikit-learn: clustering examples]]\\
 Events: Feast of Ramadan holiday (Apr 21-23), National Sovereignty and Children's Day holiday (Apr 23)\\ Events: Feast of Ramadan holiday (Apr 21-23), National Sovereignty and Children's Day holiday (Apr 23)\\
  
 +{{ :ge461_supervisedlearning_part1.pdf |{{ :ge461_supervisedlearning_part2.pdf |}}}}
 ==== Week 12 (Apr 24, Apr 26) ====  ==== Week 12 (Apr 24, Apr 26) ==== 
 ** Machine learning; supervised learning; classifiers; deep learning. ** [Dündar]\\ ** Machine learning; supervised learning; classifiers; deep learning. ** [Dündar]\\
 Topic Details: Bayesian decision theory, linear discriminants, introduction to neural networks, support vector machines, decision trees.\\ Topic Details: Bayesian decision theory, linear discriminants, introduction to neural networks, support vector machines, decision trees.\\
-Slides and Additional Material: \\+Slides and Additional Material: {{ :ge461_supervisedlearning_part1.pdf |}}\\
 Project/Exercise-Problem-Set/Homework: \\ Project/Exercise-Problem-Set/Homework: \\
 References: \\ References: \\
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 ** Machine learning; supervised learning; classifiers; deep learning. ** [Dündar]\\ ** Machine learning; supervised learning; classifiers; deep learning. ** [Dündar]\\
 Topic Details: Bayesian decision theory, linear discriminants, introduction to neural networks, support vector machines, decision trees.\\ Topic Details: Bayesian decision theory, linear discriminants, introduction to neural networks, support vector machines, decision trees.\\
-Slides and Additional Material: \\+Slides and Additional Material: {{ :ge461_supervisedlearning_part2.pdf |}} \\
 Project/Exercise-Problem-Set/Homework: \\ Project/Exercise-Problem-Set/Homework: \\
 References: \\ References: \\
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 ** Machine learning; supervised learning; classifiers; deep learning.** [Dibeklioğlu] \\ ** Machine learning; supervised learning; classifiers; deep learning.** [Dibeklioğlu] \\
 Topic Details: Activation functions, convolutional neural networks, recurrent architectures.\\ Topic Details: Activation functions, convolutional neural networks, recurrent architectures.\\
-Slides and Additional Material: \\  +Slides and Additional Material: {{ ::ge461_deep_learning_2023s.pdf |}} \\ 
-Project/Exercise-Problem-Set/Homework:\\+Project/Exercise-Problem-Set/Homework:[{{ :GE461_project_supervised_learning_2023s.pdf |Project Description}} | {{ :data_supervised_learning_project.zip |Data}}]  (due 23:55 on May 22, 2023)\\
 References: \\ References: \\
 Events: \\ Events: \\
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 ** Machine learning in healthcare. ** [Çukur] \\ ** Machine learning in healthcare. ** [Çukur] \\
 Topic Details: Healthcare analytics: diagnostics, medical imaging, in-patient care, hospital management, risk analytics, wearables. Deep learning architectures for medical applications; \\ Topic Details: Healthcare analytics: diagnostics, medical imaging, in-patient care, hospital management, risk analytics, wearables. Deep learning architectures for medical applications; \\
-Slides and Additional Material: \\ +Slides and Additional Material: {{ :ge461_ml_in_healthcare.pdf |}}\\ 
-Project/Exercise-Problem-Set/Homework:\\+Project: {{ :ge461_pw13_description.pdf |}} {{ :ge461_pw13_data.zip |}}\\
 References: Hastie, Tibshirani and Friedman, The Elements of Statistical Learning, Ch. 11 and 14; Mead, Analog VLSI and Neural Systems, Ch. 4; Bishop, Pattern Recognition and Machine Learning, Ch. 5\\ References: Hastie, Tibshirani and Friedman, The Elements of Statistical Learning, Ch. 11 and 14; Mead, Analog VLSI and Neural Systems, Ch. 4; Bishop, Pattern Recognition and Machine Learning, Ch. 5\\
 Events: \\ Events: \\
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 ** Data mining; online data stream classification; applications.**  [Can] \\ ** Data mining; online data stream classification; applications.**  [Can] \\
 Topic Details: Concept drift, ensemble-based classification, text mining. \\ Topic Details: Concept drift, ensemble-based classification, text mining. \\
-Slides and Additional Material:\\ +Slides and Additional Material: {{ :GE461_dataStreamMiningSpring23.pdf |}} {{ :GE461_dataStreamHWspringVer2_2023.pdf |}}\\ 
-Project/Exercise-Problem-Set/Homework:\\+Project/Exercise-Problem-Set/Homework: {{ ge461_datastreamhwspringver1_2023_2.pdf |}}\\
 References:  \\ References:  \\
 Events: \\ Events: \\
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 ==== Week 17 (May 29, May 31) ====  ==== Week 17 (May 29, May 31) ==== 
 ** Reinforcement learning; applications.  ** [Tekin] \\ ** Reinforcement learning; applications.  ** [Tekin] \\
-Topic Details: Applications of Reinforcement Learning, Markov Decision Processes, Value Iteration, Q Learning\\ +Topic Details: Applications of Reinforcement Learning, Markov Decision Processes, Value Iteration, Q Learning, Multi-armed bandits \\ 
-Slides and Additional Material: \\+Slides and Additional Material: https://www.dropbox.com/s/65h9melvnvuml2x/ge461_reinforcementlearning.pdf?dl=0 \\
 Project/Exercise-Problem-Set/Homework: \\ Project/Exercise-Problem-Set/Homework: \\
 References: \\ References: \\
start.1680361050.txt.gz · Last modified: 2023/04/01 14:57 by ge461

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