From e8c631ca3484ea716989db3aea112240f7999423 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Thu, 4 Nov 2021 17:06:24 +0100 Subject: [PATCH] update on video --- README.md | 1 + doc/LectureNotes/schedule.md | 15 ++++++++------- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index f11157190..1095f1aa4 100644 --- a/README.md +++ b/README.md @@ -375,6 +375,7 @@ Recommended prereading: Chapters 1-2 (linear algebra) and chapter 3 (statistics) ### Week 44 November 1-5 - Lab Wednesday: Work on project 2 - Lecture Thursday: Summary on PCA and discussion of Clustering for unsupervised learning. Decision trees, classification and regression + - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureNovember4.mp4?vrtx=view-as-webpage - Lecture Friday: Decision trees, basic algorithms - Reading recommendations: - See lecture notes for week 44 at https://compphysics.github.io/MachineLearning/doc/web/course.html. diff --git a/doc/LectureNotes/schedule.md b/doc/LectureNotes/schedule.md index cda1f3f89..fbb84248e 100644 --- a/doc/LectureNotes/schedule.md +++ b/doc/LectureNotes/schedule.md @@ -135,23 +135,23 @@ For the reading assignments we use the following abbreviations: - Lab Wednesday: Work on project 2 - Lecture Thursday: Recurrent Neural Networks - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober28.mp4?vrtx=view-as-webpage -- Lecture Friday: Recurrent Neural Networks and principal component analysis (PCA) - - Video at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober29.mp4?vrtx=view-as-webpage +- Lecture Friday: Recurrent Neural Networks and time series and principal component analysis (PCA) + - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober29.mp4?vrtx=view-as-webpage - 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 - - For PCA see lecture notes (jupyter book) chapter 11 and also chapter 12 on clustering. Geron's chapter 8 is also a good read. - + - For PCA, see lecture notes chapter 11 and Geron's text chapter 8 ### Week 44 November 1-5 - Lab Wednesday: Work on project 2 -- Lecture Thursday: Decision trees, classification and regression +- Lecture Thursday: Summary on PCA and discussion of Clustering for unsupervised learning. Decision trees, classification and regression + - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureNovember4.mp4?vrtx=view-as-webpage - 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 + - Hastie et al sections 9.1 and 9.2. Geron's text chapter 6 (Decision trees) and chapter 8 on PCA and Clustering ### Week 45 November 8-12 -- Lab Wednesday: Work on project 2 and start project 3 +- Lab Wednesday: Work on project 2, project 3 available. Deadline project 2 is November 15. - Lecture Thursday: Ensemble methods, bagging and random forests - Lecture Friday: Boosting and gradient boosting - Reading recommendations: @@ -182,3 +182,4 @@ For the reading assignments we use the following abbreviations: +