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CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html + +- Reading recommendations: + - Refresh linear algebra, GBC chapters 1 and 2. + - CMB sections 1.1 and 3.1. + - HTF chapters 2 and 3. + - See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html ### Week 35 August 30-September 3 - Lab Wednesday: Work on exercises 1-3 for week 35 - Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression and Singular Value Decomposition -- Video of lecture Thursday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage". +- Video of lecture Thursday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage. - Friday: Analysis of Ridge and Lasso Regression and links with Singular Value Decomposition -- Video of lecture Friday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember3.mp4?vrtx=view-as-webpage" -- Reading recommendations: See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html. HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1 and CMB sections 1.1 and 3.1 +- Video of lecture Friday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember3.mp4?vrtx=view-as-webpage + +- Reading recommendations: + - See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1 + - CMB sections 1.1 and 3.1 ### Week 36 September 6-10 - Lab Wednesday: Exercises 1 and 2 from week 36 - Lecture Thursday: Summary from last week on SVD, Statistics, probability theory and linear regression -- Video of Lecture https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember9.mp4?vrtx=view-as-webpage". +- Video of Lecture https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember9.mp4?vrtx=view-as-webpage - Friday: Linear Regression and links with Statistics, Resampling methods and presentation of first project. -- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember10.mp4?vrtx=view-as-webpage" +- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember10.mp4?vrtx=view-as-webpage -- Recommended Reading: Lectures on Regression, Bishop 1.1, 1.2, 2.1, 2.2, 2.3 and 3.1, Hastie et al chapter 3 +- Reading recommendations: + - Lectures on Regression for week 36 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - Bishop 1.1, 1.2, 2.1, 2.2, 2.3 and 3.1 + - Hastie et al chapter 3 ### Week 37 September 13-17 -- Lab Wednesday: +- Lab Wednesday: Work on Project 1 - Lecture Thursday: Resampling methods, cross-validation and Bootstrap - Lecture Friday: More on Resampling methods and summary of linear regression + - Reading recommendations: -- Recommended Reading: - Lectures on Resampling methods for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html. - Bishop 1.3 (cross-validation) and 3.2 (bias-variance tradeoff) - - Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) and 7.11 (bootstrap). This chapter is better than Bishop's on these topics. Goodfellow et al discuss some of these topics in sections 5.2-5.5. + - Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) and 7.11 (bootstrap) + - Goodfellow et al discuss some of these topics in sections 5.2-5.5. ### Week 38 September 20-24 -- Lab Wednesday: +- Lab Wednesday: Work on Project 1 - Lecture Thursday: Classification problems and Logistic Regression, from binary cases to several categories - Lecture Friday: Logistic Regression and gradient optimization -- Reading recommendations: See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + +- Reading recommendations: + - See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html. - Chapter ### Week 39 September 27- October 1 -- Lab Wednesday: +- Lab Wednesday: Work on Project 1 - Lecture Thursday: Gradient Optimization methods - Lecture Friday: Deep Learning and Neural Networks -- Reading recommendations: See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html. - - Chapter + +- Reading recommendations: + - See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + ### Week 40 October 4-8 -- Lab Wednesday: +- Lab Wednesday: Wrap up project 1 and start project 2 - Lecture Thursday: Writing a feed-forward Neural Network code for regression and classification - Lecture Friday: Deep Learning and TensorFlow and Keras - Reading recommendations: - - Chapter + ### Week 41 October 11-15 - Lab Wednesday: - Lecture Thursday: Deep learning and Neural Networks @@ -88,43 +105,43 @@ For the reading assignments we use the following abbreviations: - Lecture Thursday: Convolutional Neural Networks and classification problems - Lecture Friday: Convolutional Neural Networks and classification problems - Reading recommendations: - - Chapter + ### Week 43 October 25-29 - Lab Wednesday: - Lecture Thursday: Recurrent Neural Networks - Lecture Friday: Recurrent Neural Networks and time series - Reading recommendations: - - Chapter + ### Week 44 November 1-5 - Lab Wednesday: - Lecture Thursday: Decision trees, classification and regression - Lecture Friday: Decision trees, basic algorithms - Reading recommendations: - - Chapter + ### Week 45 November 8-12 - Lab Wednesday: - Lecture Thursday: Ensemble methods, bagging and random forests - Lecture Friday: Boosting and gradient boosting - Reading recommendations: - - Chapter + ### Week 46 November 15-19 - Lab Wednesday: - Lecture Thursday: - Lecture Friday: Unsupervised Learning, k-means - Reading recommendations: - - Chapter + ### Week 47 November 22-26 - Lab Wednesday: - Lecture Thursday: Unsupervised Learning, Principal Component Analysis (PCA) - Lecture Friday: Unsupervised Learning and PCA and Clustering - Reading recommendations: - - Chapter + ### Week 48 November 29- December 2 - Lab Wednesday: - Lecture Thursday: Unsupervised Learning and Clustering - Lecture Friday: Summary of course - Reading recommendations: - - Chapter + diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html index 8b38d419e..e16181189 100644 --- a/doc/LectureNotes/_build/html/schedule.html +++ b/doc/LectureNotes/_build/html/schedule.html @@ -412,7 +412,14 @@

  • Video of Lecture August 26, 2021 at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureThursdayAugust26.mp4?vrtx=view-as-webpage

  • Lecture Friday: Basics of Linear Regression

  • Video of Lecture August 27, 2021 at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureThursdayAugust27.mp4?vrtx=view-as-webpage

    • -

  • Reading recommendations: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html

    • +

  • Reading recommendations:

    + +
    • @@ -420,10 +427,16 @@
    @@ -431,24 +444,30 @@

    Week 37 September 13-17

      -

    • Lab Wednesday:

      • +

    • Lab Wednesday: Work on Project 1

    • Lecture Thursday: Resampling methods, cross-validation and Bootstrap

    • Lecture Friday: More on Resampling methods and summary of linear regression

      • -

    • Reading recommendations:

      • -

    • Recommended Reading:

      +

    • Reading recommendations:

      • Lectures on Resampling methods for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html.

      • Bishop 1.3 (cross-validation) and 3.2 (bias-variance tradeoff)

        • -

      • Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) and 7.11 (bootstrap). This chapter is better than Bishop’s on these topics. Goodfellow et al discuss some of these topics in sections 5.2-5.5.

        • +

      • Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) and 7.11 (bootstrap)

        • +

      • Goodfellow et al discuss some of these topics in sections 5.2-5.5.

    @@ -456,11 +475,12 @@

    Week 38 September 20-24

      -

    • Lab Wednesday:

      • +

    • Lab Wednesday: Work on Project 1

    • Lecture Thursday: Classification problems and Logistic Regression, from binary cases to several categories

    • Lecture Friday: Logistic Regression and gradient optimization

      • -

    • Reading recommendations: See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.

      +

    • Reading recommendations:

      • @@ -469,12 +489,12 @@

      Week 39 September 27- October 1

      @@ -482,14 +502,10 @@

      Week 40 October 4-8

        -

      • Lab Wednesday:

        • +

      • Lab Wednesday: Wrap up project 1 and start project 2

      • Lecture Thursday: Writing a feed-forward Neural Network code for regression and classification

      • Lecture Friday: Deep Learning and TensorFlow and Keras

        • -

      • Reading recommendations:

        -
          -

        • Chapter

          • -
        -
        • +

      • Reading recommendations:

      @@ -511,11 +527,7 @@

    • Lab Wednesday:

    • Lecture Thursday: Convolutional Neural Networks and classification problems

    • Lecture Friday: Convolutional Neural Networks and classification problems

      • -

    • Reading recommendations:

      -
        -

      • Chapter

        • -
      -
      • +

    • Reading recommendations:

    @@ -524,11 +536,7 @@

  • Lab Wednesday:

  • Lecture Thursday: Recurrent Neural Networks

  • Lecture Friday: Recurrent Neural Networks and time series

    • -

  • Reading recommendations:

    -
      -

    • Chapter

      • -
    -
    • +

  • Reading recommendations:

    • @@ -537,11 +545,7 @@

  • Lab Wednesday:

  • Lecture Thursday: Decision trees, classification and regression

  • Lecture Friday: Decision trees, basic algorithms

    • -

  • Reading recommendations:

    -
      -

    • Chapter

      • -
    -
    • +

  • Reading recommendations:

    • @@ -550,11 +554,7 @@

  • Lab Wednesday:

  • Lecture Thursday: Ensemble methods, bagging and random forests

  • Lecture Friday: Boosting and gradient boosting

    • -

  • Reading recommendations:

    -
      -

    • Chapter

      • -
    -
    • +

  • Reading recommendations:

    • @@ -563,11 +563,7 @@

  • Lab Wednesday:

  • Lecture Thursday:

  • Lecture Friday: Unsupervised Learning, k-means

    • -

  • Reading recommendations:

    -
      -

    • Chapter

      • -
    -
    • +

  • Reading recommendations:

    • @@ -576,11 +572,7 @@

  • Lab Wednesday:

  • Lecture Thursday: Unsupervised Learning, Principal Component Analysis (PCA)

  • Lecture Friday: Unsupervised Learning and PCA and Clustering

    • -

  • Reading recommendations:

    -
      -

    • Chapter

      • -
    -
    • +

  • Reading recommendations:

    • @@ -589,11 +581,7 @@

  • Lab Wednesday:

  • Lecture Thursday: Unsupervised Learning and Clustering

  • Lecture Friday: Summary of course

    • -

  • Reading recommendations:

    -
      -

    • Chapter

      • -
    -
    • +

  • Reading recommendations:

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Clustering Analysis","3. Linear Regression","14. Building a Feed Forward Neural Network","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","Content in Jupyter Book","Applied Data Analysis and Machine Learning, FYS-STK3155/4155 at the University of Oslo, Norway","2. Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","1. 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Video of Lecture August 26, 2021 at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureThursdayAugust26.mp4?vrtx=view-as-webpage - Lecture Friday: Basics of Linear Regression - Video of Lecture August 27, 2021 at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureThursdayAugust27.mp4?vrtx=view-as-webpage -- Reading recommendations: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html + +- Reading recommendations: + - Refresh linear algebra, GBC chapters 1 and 2. + - CMB sections 1.1 and 3.1. + - HTF chapters 2 and 3. + - See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html ### Week 35 August 30-September 3 - Lab Wednesday: Work on exercises 1-3 for week 35 - Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression and Singular Value Decomposition -- Video of lecture Thursday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage". +- Video of lecture Thursday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage. - Friday: Analysis of Ridge and Lasso Regression and links with Singular Value Decomposition -- Video of lecture Friday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember3.mp4?vrtx=view-as-webpage" -- Reading recommendations: See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html. HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1 and CMB sections 1.1 and 3.1 +- Video of lecture Friday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember3.mp4?vrtx=view-as-webpage + +- Reading recommendations: + - See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1 + - CMB sections 1.1 and 3.1 ### Week 36 September 6-10 - Lab Wednesday: Exercises 1 and 2 from week 36 - Lecture Thursday: Summary from last week on SVD, Statistics, probability theory and linear regression -- Video of Lecture https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember9.mp4?vrtx=view-as-webpage". +- Video of Lecture https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember9.mp4?vrtx=view-as-webpage - Friday: Linear Regression and links with Statistics, Resampling methods and presentation of first project. -- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember10.mp4?vrtx=view-as-webpage" +- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember10.mp4?vrtx=view-as-webpage -- Recommended Reading: Lectures on Regression, Bishop 1.1, 1.2, 2.1, 2.2, 2.3 and 3.1, Hastie et al chapter 3 +- Reading recommendations: + - Lectures on Regression for week 36 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - Bishop 1.1, 1.2, 2.1, 2.2, 2.3 and 3.1 + - Hastie et al chapter 3 ### Week 37 September 13-17 -- Lab Wednesday: +- Lab Wednesday: Work on Project 1 - Lecture Thursday: Resampling methods, cross-validation and Bootstrap - Lecture Friday: More on Resampling methods and summary of linear regression + - Reading recommendations: -- Recommended Reading: - Lectures on Resampling methods for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html. - Bishop 1.3 (cross-validation) and 3.2 (bias-variance tradeoff) - - Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) and 7.11 (bootstrap). This chapter is better than Bishop's on these topics. Goodfellow et al discuss some of these topics in sections 5.2-5.5. + - Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) and 7.11 (bootstrap) + - Goodfellow et al discuss some of these topics in sections 5.2-5.5. ### Week 38 September 20-24 -- Lab Wednesday: +- Lab Wednesday: Work on Project 1 - Lecture Thursday: Classification problems and Logistic Regression, from binary cases to several categories - Lecture Friday: Logistic Regression and gradient optimization -- Reading recommendations: See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + +- Reading recommendations: + - See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html. - Chapter ### Week 39 September 27- October 1 -- Lab Wednesday: +- Lab Wednesday: Work on Project 1 - Lecture Thursday: Gradient Optimization methods - Lecture Friday: Deep Learning and Neural Networks -- Reading recommendations: See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html. - - Chapter + +- Reading recommendations: + - See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + ### Week 40 October 4-8 -- Lab Wednesday: +- Lab Wednesday: Wrap up project 1 and start project 2 - Lecture Thursday: Writing a feed-forward Neural Network code for regression and classification - Lecture Friday: Deep Learning and TensorFlow and Keras - Reading recommendations: - - Chapter + ### Week 41 October 11-15 - Lab Wednesday: - Lecture Thursday: Deep learning and Neural Networks @@ -88,43 +105,43 @@ For the reading assignments we use the following abbreviations: - Lecture Thursday: Convolutional Neural Networks and classification problems - Lecture Friday: Convolutional Neural Networks and classification problems - Reading recommendations: - - Chapter + ### Week 43 October 25-29 - Lab Wednesday: - Lecture Thursday: Recurrent Neural Networks - Lecture Friday: Recurrent Neural Networks and time series - Reading recommendations: - - Chapter + ### Week 44 November 1-5 - Lab Wednesday: - Lecture Thursday: Decision trees, classification and regression - Lecture Friday: Decision trees, basic algorithms - Reading recommendations: - - Chapter + ### Week 45 November 8-12 - Lab Wednesday: - Lecture Thursday: Ensemble methods, bagging and random forests - Lecture Friday: Boosting and gradient boosting - Reading recommendations: - - Chapter + ### Week 46 November 15-19 - Lab Wednesday: - Lecture Thursday: - Lecture Friday: Unsupervised Learning, k-means - Reading recommendations: - - Chapter + ### Week 47 November 22-26 - Lab Wednesday: - Lecture Thursday: Unsupervised Learning, Principal Component Analysis (PCA) - Lecture Friday: Unsupervised Learning and PCA and Clustering - Reading recommendations: - - Chapter + ### Week 48 November 29- December 2 - Lab Wednesday: - Lecture Thursday: Unsupervised Learning and Clustering - Lecture Friday: Summary of course - Reading recommendations: - - Chapter +