UMR CNRS 7253

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en:aec [2019/08/23 05:37] – tdenoeuxen:aec [2024/02/07 07:58] – tdenoeux
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-====== Advanced Computational Econometrics: Machine Learning ======+====== Advanced Computational Econometrics: Statistical Learning ======
  
 **Instructor:** Thierry Denoeux **Instructor:** Thierry Denoeux
  
-Description: This course is an introduction to machine learning, with examples of application to econometrics. +Description: This course is an introduction to statistical learning, with examples of application to econometrics. 
  
 **Program** **Program**
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   - Model selection    - Model selection 
   - Splines and generalized additive models    - Splines and generalized additive models 
-  - Tree-based methods  +  - Tree-based and ensemble methods  
-  - Gaussian mixture models +  - Kernel-based classification and regression 
-  - Support Vector Machines  +
-  - Support Vector Regression and KPCA +
-  - Relevance Vector Machines+
   - Neural networks     - Neural networks  
-  
  
 **Recommended reading**:  **Recommended reading**: 
  
-  * [[https://web.stanford.edu/~hastie/ElemStatLearn/|The Elements of Statistical Learning: Data Mining, Inference, and Prediction]] by Trevor Hastie, Robert Tibshirani and Jerome Friedman +  * [[https://www.statlearning.com|An Introduction to Statistical Learning with Applications in R]] 
-  * [[https://www.springer.com/gp/book/9780387310732|Pattern Recognition and Machine Learning]] by Christopher Bishop.+  * [[https://web.stanford.edu/~hastie/ElemStatLearn/|The Elements of Statistical Learning: Data Mining, Inference, and Prediction]]  
 +  * [[https://www.springer.com/gp/book/9780387310732|Pattern Recognition and Machine Learning]] 
  
 **Slides** **Slides**
  
-  - {{ :en:ace_chapter1_2019.pdf |Introduction}} +  - {{ :en:ace_chapter1_2023.pdf | Introduction}} 
-  - {{ :en:ace_chapter2_2019.pdf |Linear classification}} +  - {{ :en:ace_chapter2_2023.pdf | Linear and Quadratic Classification}} 
-  - {{ :en:ace_chapter3_2019.pdf |Model selection}} +  - {{ :en:ace_chapter3_2023.pdf | Model Selection}} 
-  - {{ :en:ace_chapter4_2019.pdf |Splines and generalized additive models}} +  - {{ :en:ace_chapter4_2023.pdf | Splines and Generalized Additive Models}} 
-  - {{ :en:ace_chapter5_2019.pdf |Tree-based and ensemble methods}} +  - {{ :en:ace_chapter5_2023.pdf | Tree-based and ensemble methods}} 
-  - {{ :en:ace_chapter6_2019.pdf |Gaussian mixture models}} +  - {{ :en:ace_chapter6_2023.pdf | Kernel-based classification and regression}} 
-  - {{ :en:ace_chapter7_2019.pdf |Support vector machines}} +  - {{ :en:ace_chapter7_2023.pdf | Neural networks and deep learning}}
-  - {{ :en:ace_chapter8_2019.pdf |Kernel-based Regression and Feature Extraction}} +
-  - {{ :en:ace_chapter9_2019.pdf |Relevance Vector Machines}} +
-  - {{ :en:ace_chapter10_2019.pdf |Neural networks}}+
  
-**Exercises**+**Recordings**
  
-  - {{ :en:exercices_chapter1.pdf |K-NN regression and bias-variance decomposition}} +  - [[https://filesender.utc.fr/filesender/?s=download&token=baa01e2d-4afc-462b-ae99-fafa6077dab7|Lecture 1 (April 4, 2023)]] 
-    * {{ :en:chapter1_exercises.r.zip |Solution}} +  - [[https://filesender.utc.fr/filesender/?s=download&token=59ed86b7-43ae-4955-a9a0-7f06b2ae4c67|Lecture 2 (April 7, 2023)]] 
-  - {{ :en:exercises_chapter2.pdf |Linear classification}} +  - [[https://filesender.utc.fr/filesender/?s=download&token=3ff7301a-4904-47f8-97b0-93d34f2e484c|Lecture 3 (April 11, 2023)]] 
-    * {{ :en:ace_chapter2_exercises.r.zip |Solution}} +  - [[https://filesender.utc.fr/filesender/?s=download&token=97a58b51-0879-4ea6-93ef-b5647df76574|Lecture 4 (April 12, 2023)]] 
-  - {{ :en:aec_exercises_chapter3.pdf |Model selection}} +  - [[https://filesender.utc.fr/filesender/?s=download&token=969d717a-a96f-4f33-8ca5-2ea5bedc99ce|Lecture 5 (April 18, 2023)]] 
-    * {{ :en:ace_chapter3_exercises.r.zip |Solution}} +  - [[https://filesender.utc.fr/filesender/?s=download&token=0d8c00a0-7864-4898-badd-afcd4b0b449b|Lecture 6 (April 19, 2023)]] 
-  - {{ :en:ace_exercises_chapter4.pdf |Splines and GAM}} +  - [[https://filesender.utc.fr/filesender/?s=download&token=d15f39fb-c34d-47dc-a82f-539b679a387d|Lecture 7 (April 29, 2023)]] 
-    * {{ :en:ace_chapter4_exercises.r.zip |Solution}} +  - [[https://filesender.utc.fr/filesender/?s=download&token=e4143368-49de-4eec-a7cb-11f4c023b0a4|Lecture 8 (May 3, 2023)]] 
-  - {{ :en:ace_exercises_chapter5.pdf |Tree-based and ensemble methods}} +  - [[https://filesender.utc.fr/filesender/?s=download&token=330c3ebc-e7f9-4607-8970-c105b3f85760|Lecture 9 (May 5, 2023)]] 
-    * {{ :en:ace_chapter5_exercises.r.zip |Solution}} +  - [[https://filesender.utc.fr/filesender/?s=download&token=f88ade42-e95a-4a20-af1c-1baac7479227|Lecture 10 (May 11, 2023)]] 
-  - {{ :en:ace_exercises_chapter6.pdf |Gaussian mixture models}} +  - [[https://filesender.utc.fr/filesender/?s=download&token=ee0b1cd0-ee3b-4c5e-91fa-30d2c302a482|Lecture 11 (May 13, 2023)]] 
-    * {{ :en:ace_chapter6_exercises.r.zip |Solution}} +  - [[https://filesender.utc.fr/filesender/?s=download&token=ad8d44ad-e6fb-49cd-ad38-0969c6d49275|Lecture 12 (May 19, 2023)]] 
-  - {{ :en:ace_exercises_chapters7_8.pdf |Support vector classification and regression}} +  - [[https://filesender.utc.fr/filesender/?s=download&token=51e9b279-7545-41b0-b59d-23853a01872d|Lecture 13 (May 23, 2023)]] 
-    * {{ :en:ace_chapters7-9_exercises.r.zip |Solution}} +  - [[https://filesender.utc.fr/filesender/?s=download&token=7f984bbf-5dac-40d6-a730-67e033751b0f|Lecture 14 (May 24, 2023)]]
-  - {{ :en:ace_chapters10_exercises.r.zip |Neural networks: solution}}+
  
-** Exam ** 
  
-  * {{ :en:exam_2019_ace_.pdf |Final exam}}+**Exercises** 
 + 
 +  - {{ :en:ace_exercices_chapter1.pdf |K-NN regression and bias-variance decomposition}} 
 +  - {{ :en:ace_exercises_chapter2.pdf | Linear and Quadratic Classification}} 
 +  - {{ :en:ace_exercises_chapter3.pdf | Model selection}} 
 +  - {{ :en:ace_exercises_chapter4.pdf | Splines and Generalized Additive Models}} 
 +  - {{ :en:ace_exercises_chapter5.pdf | Tree-based and ensemble methods}} 
 +  - {{ :en:ace_exercises_chapters6.pdf | Kernel-based classification and regression}} 
 +  - {{ :en:ace_exercises_chapters7.pdf |Neural networks and deep learning}} 
 +  
  
 ** Datasets ** ** Datasets **
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   * {{ :en:tablef14-1.csv.zip |Program effectiveness}}   * {{ :en:tablef14-1.csv.zip |Program effectiveness}}
   * {{ :en:cp.csv.zip |CP time series}}   * {{ :en:cp.csv.zip |CP time series}}
-  * {{ :en:bike_sharing_day.csv.zip |Bike sharing}}+  * {{ :en:f7_1.zip |German health care}} 
 +  * {{ :en:communities_and_crime.zip |Communities and crime}} 
 +  * {{ :en:bike_sharing.rdata.zip |Bike sharing}}

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en/aec.txt · Last modified: 2024/02/07 07:59 by tdenoeux
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