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@@ -231,30 +231,45 @@ For the reading assignments we use the following abbreviations:
Recommended prereading: Chapters 1-2 (linear algebra) and chapter 3 (statistics) of Goodfellow et al. and Bishop chapter 1 and chapter 2. These chapters give a relevant background to the basic mathematical and statistical foundations of the course. Parts of these chapters will be covered during the lectures the first three weeks.
### Week 34 August 23-27
- Lab Wednesday: Introduction to software and repetition of Python Programming
- Lecture Thursday: Introduction to the course, what is Machine Learning and introduction to Linear Regression
- Lecture Thursday: Introduction to the course, what is Machine Learning and introduction to Linear Regression.
- 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
### Week 35 August 30-September 3
- Lab Wednesday:
- Lecture Thursday: Linear Regression, from ordinary linear regression to Ridge and Lasso regression, linear algebra analysis, examples and discussions of codes
- Lecture Friday: Linear Regression, Linear algebra and Ridge and Lasso Regression, linear algebra analysis, examples and discussions of codes
- 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".
- 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
### Week 36 September 6-10
- Lab Wednesday:
- Lecture Thursday: Ridge and Lasso regression and the SVD. Statistical interpretation of Linear Regression
- Lecture Friday: Further interpretations of Linear regression.
- Reading recommendations: See lecture notes for week 36 at https://compphysics.github.io/MachineLearning/doc/web/course.html. GBC sections 5.2-5.5, CMB section 3.2
- Chapter
- 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".
- 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"
- Recommended Reading: Lectures on Regression, 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:
- Lecture Thursday: Resampling methods, cross-validation and Bootstrap
- Lecture Friday: More on Resampling methods and summary of linear regression
- Reading recommendations: See lecture notes for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Chapter
- 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.
### Week 38 September 20-24
- Lab Wednesday:
- Lecture Thursday: Classification problems and Logistic Regression, from binary cases to several categories
@@ -324,3 +339,4 @@ Recommended prereading: Chapters 1-2 (linear algebra) and chapter 3 (statistics)