GE461 Spring 2023

User Tools

Site Tools

Trace:
start

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision 		Previous revision
start [2024/03/14 10:05]
ge461 [Week 6 (Mar 4)] 		start [2024/05/21 05:04] (current)
ge461 [Week 10 (Apr 1, Apr 4)]
Line 13: Line 13:
   * Course Coordinator (contact point): S. Aksoy (saksoy AT cs.bilkent.edu.tr)   * Course Coordinator (contact point): S. Aksoy (saksoy AT cs.bilkent.edu.tr)
      
-**TAs**  +**TAs** 
-  * TBD+  * Ali Azak (ali.azak AT bilkent.edu.tr) 
 +  * Hakan Gökçesu (hgokcesu AT ee.bilkent.edu.tr)
  
 **Classroom and Hours** **Classroom and Hours**
Line 31: Line 32:
  
 ** 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**
Line 107: Line 108:
 ==== Week 6 (Mar 4) ====  ==== Week 6 (Mar 4) ==== 
 ** Application to customer choice problems (conjoint analysis) ** [S. Dayanık] \\ ** Application to customer choice problems (conjoint analysis) ** [S. Dayanık] \\
-Topic Details: Part worths, part importance, their estimations from product rankings with multiple regression\\+Topic Details: Part worths, part importance, their estimations from product rankings with multiple regression, new product design with market simulation to increase overall market share.\\
 Slides:  {{ ::s2024_week06.pdf | Conjoint Analysis and Market Simulation}}\\ Slides:  {{ ::s2024_week06.pdf | Conjoint Analysis and Market Simulation}}\\
 Project/Exercise-Problem-Set/Homework: \\ Project/Exercise-Problem-Set/Homework: \\
 References:  References: 
   * B. K. Orme, Getting Started With Conjoint Analysis: Strategies for Product Design and Pricing Research    * B. K. Orme, Getting Started With Conjoint Analysis: Strategies for Product Design and Pricing Research 
-  * Miller, Marketing Data Science: Modeling Techniques in Predictive Analytics With R and Python +  * Miller, Marketing Data Science: Modeling Techniques in Predictive Analytics With R and Python\\
-\\+
 Events: Spring Break (Mar 7-8)\\ Events: Spring Break (Mar 7-8)\\
  
 ==== Week 7 (Mar 11, Mar 14) ====  ==== Week 7 (Mar 11, Mar 14) ==== 
 ** Authorship problem, text analysis, and topic modeling ** [S. Dayanık] \\ ** Authorship problem, text analysis, and topic modeling ** [S. Dayanık] \\
-Topic Details: New product design with market simulation to increase overall market share; who wrote the Federalists papers (identiciation of authorships by means of Bayesian classifiers, kNN) \\+Topic Details: Who wrote the Federalists papers (identiciation of authorships by means of Bayesian classifiers, kNN) \\
 Slides and Additional Material:\\ Slides and Additional Material:\\
 +{{ :s2024_week07_federalist.pdf | Federalist Papers Analysis}} 
 +{{ ::s2024_week07_lda_annotated.pdf | Latent Diriclet Allocation Graphical Model}} \\
 Project/Exercise-Problem-Set/Homework:\\ Project/Exercise-Problem-Set/Homework:\\
-References: \\+References: 
 +  * Hamilton, Jay, and Madison, The Federalist Papers 
 +  * Mosteller and Wallace, Applied Bayesian and Classical Inference\\
 Events: \\ Events: \\
  
Line 127: Line 131:
 **  Dimensionality reduction; visualization.** [Aksoy] \\ **  Dimensionality reduction; visualization.** [Aksoy] \\
 Topic Details: Feature reduction, feature selection, high-dimensional data visualization.\\ Topic Details: Feature reduction, feature selection, high-dimensional data visualization.\\
-Slides and Additional Material:\\ +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-examples|Scikit-learn: decomposition examples]], [[https://scikit-learn.org/stable/modules/manifold.html|Scikit-learn: manifold learning]], [[https://lvdmaaten.github.io/tsne/|t-SNE]]\\+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]]\\
 Events: \\ Events: \\
  
Line 135: Line 140:
 ** 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: \\
  
Line 143: Line 148:
 ** 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: \\
Line 154: Line 159:
 ** 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: \\
Line 162: Line 167:
 ** 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)\\
Line 176: Line 181:
  
 ==== 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.1710410746.txt.gz · Last modified: 2024/03/14 10:05 by ge461

Page Tools

Donate Powered by PHP Valid HTML5 Valid CSS Driven by DokuWiki