From eee96f1e0d5312bc7608567c78c70df71bd45eef Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Wed, 15 Sep 2021 08:52:28 +0200 Subject: [PATCH] updating schedule --- .../_build/.doctrees/environment.pickle | Bin 177238 -> 177244 bytes .../_build/.doctrees/schedule.doctree | Bin 47583 -> 60292 bytes .../_build/html/_sources/schedule.md | 52 ++++++++--- doc/LectureNotes/_build/html/schedule.html | 87 +++++++++++++----- doc/LectureNotes/_build/html/searchindex.js | 2 +- doc/LectureNotes/schedule.md | 52 ++++++++--- 6 files changed, 140 insertions(+), 53 deletions(-) diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle index 549ba82764cfa78023685fd7a37cd4326599b869..5a152b4344cc2c2c0bdd0e3fff0027ca4d3ea646 100644 GIT binary patch literal 177244 zcmd?S378#6buVsN`)J=KTejM|wl%WVGuCFwk{3p+?U7_zk}bfJhn{dJi<@7n{ck0xsQ>Ut4Hv7kCUN_@9`mepbQOg%97YE$R`9i%~DZ7*fmePW$1(#~N7y~yXik(| 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Not all the material is relevant or will be covered. Section 4.3 is the most relevant, but 4.1 and 4.2 give interesting background readings for logistic regression + - Hastie et al 4.1, 4.2 and 4.3 on logistic regression + - For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well. + ### Week 39 September 27- October 1 - Lab Wednesday: Work on Project 1 - Lecture Thursday: Gradient Optimization methods -- Lecture Friday: Deep Learning and Neural Networks +- Lecture Friday: Gradient methods and Deep Learning and Neural Networks - Reading recommendations: - See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well. + - For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4 + ### Week 40 October 4-8 - 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: + - See lecture notes for week 40 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4 ### Week 41 October 11-15 -- Lab Wednesday: +- Lab Wednesday: Work on project 2 - Lecture Thursday: Deep learning and Neural Networks - Lecture Friday: Convolutional Neural Networks, basic elements - Reading recommendations: - - GoodFellow et al, Chapter 9 + - See lecture notes for week 41 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - For neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications + + ### Week 42 October 18-22 -- Lab Wednesday: +- Lab Wednesday: Work on project 2 - Lecture Thursday: Convolutional Neural Networks and classification problems - Lecture Friday: Convolutional Neural Networks and classification problems - Reading recommendations: + - See lecture notes for week 42 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - For neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. See also chapter 11 and 12 on practicalities and applications ### Week 43 October 25-29 -- Lab Wednesday: +- Lab Wednesday: Work on project 2 - Lecture Thursday: Recurrent Neural Networks - Lecture Friday: Recurrent Neural Networks and time series - Reading recommendations: + - See lecture notes for week 43 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - For RNNs, see Goodfellow et al chapter 10 and discussions in chapter 11 and 12 on practicalities and applications ### Week 44 November 1-5 -- Lab Wednesday: +- Lab Wednesday: Work on project 2 - Lecture Thursday: Decision trees, classification and regression - Lecture Friday: Decision trees, basic algorithms - Reading recommendations: + - See lecture notes for week 44 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - Hastie et al sections 9.1 and 9.2 ### Week 45 November 8-12 -- Lab Wednesday: +- Lab Wednesday: Work on project 3 - Lecture Thursday: Ensemble methods, bagging and random forests - Lecture Friday: Boosting and gradient boosting - Reading recommendations: + - See lecture notes for week 45 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - Hastie et al chapter 10 ### Week 46 November 15-19 -- Lab Wednesday: -- Lecture Thursday: -- Lecture Friday: Unsupervised Learning, k-means +- Lab Wednesday: Work on project 3 +- Lecture Thursday: Support Vector machines +- Lecture Friday: Support Vector machines - Reading recommendations: + - See lecture notes for week 46 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - Hastie et al chapter 12 ### Week 47 November 22-26 -- Lab Wednesday: +- Lab Wednesday: Work on project 3 - Lecture Thursday: Unsupervised Learning, Principal Component Analysis (PCA) -- Lecture Friday: Unsupervised Learning and PCA and Clustering +- Lecture Friday: Unsupervised Learning and PCA, k-means and Clustering - Reading recommendations: + - See lecture notes for week 47 at https://compphysics.github.io/MachineLearning/doc/web/course.html. ### Week 48 November 29- December 2 -- Lab Wednesday: +- Lab Wednesday: Work on project 3 - Lecture Thursday: Unsupervised Learning and Clustering - Lecture Friday: Summary of course - Reading recommendations: - + - See lecture notes for week 48 at https://compphysics.github.io/MachineLearning/doc/web/course.html. diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html index e16181189..3bb7c59ac 100644 --- a/doc/LectureNotes/_build/html/schedule.html +++ b/doc/LectureNotes/_build/html/schedule.html @@ -481,7 +481,9 @@

  • Reading recommendations:

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

      • -

    • Chapter

      • +

    • Bishop 4.1, 4.2 and 4.3. Not all the material is relevant or will be covered. Section 4.3 is the most relevant, but 4.1 and 4.2 give interesting background readings for logistic regression

      • +

    • Hastie et al 4.1, 4.2 and 4.3 on logistic regression

      • +

    • For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.

    • @@ -491,10 +493,12 @@

    • Lab Wednesday: Work on Project 1

    • Lecture Thursday: Gradient Optimization methods

      • -

    • Lecture Friday: Deep Learning and Neural Networks

      • +

    • Lecture Friday: Gradient methods and Deep Learning and Neural Networks

    • Reading recommendations:

      • See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.

        • +

      • For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.

        • +

      • For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4

    @@ -505,18 +509,24 @@

  • 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:

    • +

  • Reading recommendations:

    + +

    • Week 41 October 11-15¶

      -

    • Lab Wednesday:

      • +

    • Lab Wednesday: Work on project 2

    • Lecture Thursday: Deep learning and Neural Networks

    • Lecture Friday: Convolutional Neural Networks, basic elements

    • Reading recommendations:

    @@ -524,64 +534,97 @@

    Week 42 October 18-22¶

      -

    • Lab Wednesday:

      • +

    • Lab Wednesday: Work on project 2

    • Lecture Thursday: Convolutional Neural Networks and classification problems

    • Lecture Friday: Convolutional Neural Networks and classification problems

      • -

    • Reading recommendations:

      • +

    • Reading recommendations:

      + +

    Week 43 October 25-29¶

      -

    • Lab Wednesday:

      • +

    • Lab Wednesday: Work on project 2

    • Lecture Thursday: Recurrent Neural Networks

    • Lecture Friday: Recurrent Neural Networks and time series

      • -

    • Reading recommendations:

      • +

    • Reading recommendations:

      + +

    Week 44 November 1-5¶

      -

    • Lab Wednesday:

      • +

    • Lab Wednesday: Work on project 2

    • Lecture Thursday: Decision trees, classification and regression

    • Lecture Friday: Decision trees, basic algorithms

      • -

    • Reading recommendations:

      • +

    • Reading recommendations:

      + +

    Week 45 November 8-12¶

      -

    • Lab Wednesday:

      • +

    • Lab Wednesday: Work on project 3

    • Lecture Thursday: Ensemble methods, bagging and random forests

    • Lecture Friday: Boosting and gradient boosting

      • -

    • Reading recommendations:

      • +

    • Reading recommendations:

      + +

    Week 46 November 15-19¶

      -

    • Lab Wednesday:

      • -

    • Lecture Thursday:

      • -

    • Lecture Friday: Unsupervised Learning, k-means

      • -

    • Reading recommendations:

      • +

    • Lab Wednesday: Work on project 3

      • +

    • Lecture Thursday: Support Vector machines

      • +

    • Lecture Friday: Support Vector machines

      • +

    • Reading recommendations:

      + +

    Week 47 November 22-26¶

      -

    • Lab Wednesday:

      • +

    • Lab Wednesday: Work on project 3

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

      • -

    • Lecture Friday: Unsupervised Learning and PCA and Clustering

      • -

    • Reading recommendations:

      • +

    • Lecture Friday: Unsupervised Learning and PCA, k-means and Clustering

      • +

    • Reading recommendations:

      + +

    Week 48 November 29- December 2¶

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Reading recommendations: - See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html. - - Chapter + - Bishop 4.1, 4.2 and 4.3. Not all the material is relevant or will be covered. Section 4.3 is the most relevant, but 4.1 and 4.2 give interesting background readings for logistic regression + - Hastie et al 4.1, 4.2 and 4.3 on logistic regression + - For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well. + ### Week 39 September 27- October 1 - Lab Wednesday: Work on Project 1 - Lecture Thursday: Gradient Optimization methods -- Lecture Friday: Deep Learning and Neural Networks +- Lecture Friday: Gradient methods and Deep Learning and Neural Networks - Reading recommendations: - See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well. + - For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4 + ### Week 40 October 4-8 - 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: + - See lecture notes for week 40 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4 ### Week 41 October 11-15 -- Lab Wednesday: +- Lab Wednesday: Work on project 2 - Lecture Thursday: Deep learning and Neural Networks - Lecture Friday: Convolutional Neural Networks, basic elements - Reading recommendations: - - GoodFellow et al, Chapter 9 + - See lecture notes for week 41 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - For neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications + + ### Week 42 October 18-22 -- Lab Wednesday: +- Lab Wednesday: Work on project 2 - Lecture Thursday: Convolutional Neural Networks and classification problems - Lecture Friday: Convolutional Neural Networks and classification problems - Reading recommendations: + - See lecture notes for week 42 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - For neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. See also chapter 11 and 12 on practicalities and applications ### Week 43 October 25-29 -- Lab Wednesday: +- Lab Wednesday: Work on project 2 - Lecture Thursday: Recurrent Neural Networks - Lecture Friday: Recurrent Neural Networks and time series - Reading recommendations: + - See lecture notes for week 43 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - For RNNs, see Goodfellow et al chapter 10 and discussions in chapter 11 and 12 on practicalities and applications ### Week 44 November 1-5 -- Lab Wednesday: +- Lab Wednesday: Work on project 2 - Lecture Thursday: Decision trees, classification and regression - Lecture Friday: Decision trees, basic algorithms - Reading recommendations: + - See lecture notes for week 44 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - Hastie et al sections 9.1 and 9.2 ### Week 45 November 8-12 -- Lab Wednesday: +- Lab Wednesday: Work on project 3 - Lecture Thursday: Ensemble methods, bagging and random forests - Lecture Friday: Boosting and gradient boosting - Reading recommendations: + - See lecture notes for week 45 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - Hastie et al chapter 10 ### Week 46 November 15-19 -- Lab Wednesday: -- Lecture Thursday: -- Lecture Friday: Unsupervised Learning, k-means +- Lab Wednesday: Work on project 3 +- Lecture Thursday: Support Vector machines +- Lecture Friday: Support Vector machines - Reading recommendations: + - See lecture notes for week 46 at https://compphysics.github.io/MachineLearning/doc/web/course.html. + - Hastie et al chapter 12 ### Week 47 November 22-26 -- Lab Wednesday: +- Lab Wednesday: Work on project 3 - Lecture Thursday: Unsupervised Learning, Principal Component Analysis (PCA) -- Lecture Friday: Unsupervised Learning and PCA and Clustering +- Lecture Friday: Unsupervised Learning and PCA, k-means and Clustering - Reading recommendations: + - See lecture notes for week 47 at https://compphysics.github.io/MachineLearning/doc/web/course.html. ### Week 48 November 29- December 2 -- Lab Wednesday: +- Lab Wednesday: Work on project 3 - Lecture Thursday: Unsupervised Learning and Clustering - Lecture Friday: Summary of course - Reading recommendations: - + - See lecture notes for week 48 at https://compphysics.github.io/MachineLearning/doc/web/course.html.