UMR CNRS 7253

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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**
  
   - Introduction to machine learning    - Introduction to machine learning 
-  - Linear classification  
   - 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  +  - Neural networks and deep learning 
-  - Support Vector Regression and KPCA +
-  - Relevance Vector Machines +
-  - 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_2024.pdf | Introduction}} 
-  - {{ :en:ace_chapter2_2019.pdf |Linear classification}} +  - {{ :en:ace_chapter2_2024.pdf | Model selection}} 
-  - {{ :en:ace_chapter3_2019.pdf |Model selection}} +  - {{ :en:ace_chapter3_2024.pdf | Splines and generalized additive models}} 
-  - {{ :en:ace_chapter4_2019.pdf |Splines and generalized additive models}} +  - {{ :en:ace_chapter4_2024.pdf | Tree-based and ensemble methods}} 
-  - {{ :en:ace_chapter5_2019.pdf |Tree-based and ensemble methods}} +  - {{ :en:ace_chapter5_2024.pdf | Kernel-based classification and regression}} 
-  - {{ :en:ace_chapter6_2019.pdf |Gaussian mixture models}} +  - {{ :en:ace_chapter6_2024.pdf | Neural networks and deep learning}}
-  - {{ :en:ace_chapter7_2019.pdf |Support vector machines}} +
-  - {{ :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** 
  
-  - {{ :en:exercices_chapter1.pdf |K-NN regression and bias-variance decomposition}} 
-    * {{ :en:chapter1_exercises.r.zip |Solution}} 
-  - {{ :en:exercises_chapter2.pdf |Linear classification}} 
-    * {{ :en:ace_chapter2_exercises.r.zip |Solution}} 
-  - {{ :en:aec_exercises_chapter3.pdf |Model selection}} 
-    * {{ :en:ace_chapter3_exercises.r.zip |Solution}} 
-  - {{ :en:ace_exercises_chapter4.pdf |Splines and GAM}} 
-    * {{ :en:ace_chapter4_exercises.r.zip |Solution}} 
-  - {{ :en:ace_exercises_chapter5.pdf |Tree-based and ensemble methods}} 
-    * {{ :en:ace_chapter5_exercises.r.zip |Solution}} 
-  - {{ :en:ace_exercises_chapter6.pdf |Gaussian mixture models}} 
-    * {{ :en:ace_chapter6_exercises.r.zip |Solution}} 
-  - {{ :en:ace_exercises_chapters7_8.pdf |Support vector classification and regression}} 
-    * {{ :en:ace_chapters7-9_exercises.r.zip |Solution}} 
-  - {{ :en:ace_chapter10_exercise1.r.zip |Neural networks: solution of exercise 1 (regression)}} 
  
-** Exam **+**Exercises** 
 + 
 +  - {{ :en:ace_exercices_chapter1.pdf |K-NN regression and bias-variance decomposition}} 
 +    - {{ :en:ace_solutions_chapter1.pdf |Solutions}} 
 +  - {{ :en:ace_exercises_chapter2.pdf |Model selection}} 
 +    - {{ :en:ace_solutions_chapter2.pdf |Solutions}} 
 +  - {{ :en:ace_exercises_chapter3.pdf |Splines and generalized additive models}} 
 +    - {{ :en:ace_solutions_chapter3.pdf |Solutions}} 
 +  - {{ :en:ace_exercises_chapter4.pdf |Tree-based and ensemble methods}} 
 +    - {{ :en:ace_solutions_chapter4.pdf |Solutions}} 
 +  - {{ :en:ace_exercises_chapters5.pdf |Kernel-based classification and regression}} 
 +    - {{ :en:ace_solutions_chapter5.pdf |Solutions}} 
 +  - {{ :en:ace_exercises_chapters6.pdf |Neural networks and deep learning}} 
 +    - {{ :en:ace_solutions_chapter6.pdf |Solutions}} 
 +  - {{ :en:ace_final_problem.pdf |Final problem (August 21, 2024)}}
  
-  - {{ :en:exam_2019_ace_.pdf |Final exam}}+**Exam**   
 +  - {{ :en:exam_2024_ace.pdf |Final exam}}
  
 ** 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 (long version)}} 
 +  * {{ :en:crime.zip |Crime (short version)}} 
 +  * {{ :en:bike_sharing.rdata.zip |Bike sharing data}}

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en/aec.1566178357.txt.gz · Last modified: 2019/08/19 03:32 by tdenoeux
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