GE461 Spring 2023

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start [2024/03/17 18:08]
ge461 		start [2024/05/21 05:04] (current)
ge461 [Week 10 (Apr 1, Apr 4)]
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 ** Exam** ** Exam**
-  * TBD+  * The final exam will be held at EA-Z01 (for lastnames in the range AAMIR-KOŞAY) and EA-Z03 (for lastnames in the range OĞUZTÜZÜN-YÜZLÜ) during 18:00-20:00 on May 23, 2024.
  
 ** 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}} ({{ :fashion_mnist.zip |data}})]  (due 23:59 on April 7, 2024)\\
 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]]\\+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: \\ Events: \\
  
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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_part1.pdf |supervisedlearning_part1}}, {{ :ge461_supervisedlearning_part2.pdf | supervisedlearning_part2}}\\
 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_2024s.pdf |}} \\ 
-Project/Exercise-Problem-Set/Homework:\\+Project/Exercise-Problem-Set/Homework:[{{ :GE461_project_supervised_learning_2024s.pdf |Project Description}} | {{ :data_supervised_learning_project.zip |Data}}]  (due 23:55 on April 27, 2024)\\
 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/Exercise-Problem-Set/Homework: {{ :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: National Sovereignty and Children's Day (Apr 23)\\ Events: National Sovereignty and Children's Day (Apr 23)\\
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 ==== Week 15 (May 6, May 9) ====  ==== Week 15 (May 6, May 9) ==== 
 +
 +==== Week 16 (May 13, May 16) ==== 
 ** 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\\
-Slides and Additional Material: \\+Slides and Additional Material: {{ :ge461_reinforcementlearning.pdf |}} \\
 Project/Exercise-Problem-Set/Homework: \\ Project/Exercise-Problem-Set/Homework: \\
 References: \\ References: \\
 Events: \\ Events: \\
  
-==== Week 16 (May 13, May 16) ====  
-** To be used if needed ** 
- 
----- 
  
 ==== Textbooks ==== ==== Textbooks ====
start.1710698889.txt.gz · Last modified: 2024/03/17 18:08 by ge461

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