updated schedule
This commit is contained in:
Morten Hjorth-Jensen
@@ -28,55 +28,72 @@ For the reading assignments we use the following abbreviations:
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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
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- Lecture Friday: Basics of Linear Regression
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- 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
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- 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
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- Reading recommendations:
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- Refresh linear algebra, GBC chapters 1 and 2.
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- CMB sections 1.1 and 3.1.
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- HTF chapters 2 and 3.
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- See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html
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### Week 35 August 30-September 3
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- Lab Wednesday: Work on exercises 1-3 for week 35
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- Thursday: Review of ordinary Least Squares with applications and discussion of Ridge Regression and Singular Value Decomposition
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- Video of lecture Thursday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage".
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- Video of lecture Thursday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember2.mp4?vrtx=view-as-webpage.
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- Friday: Analysis of Ridge and Lasso Regression and links with Singular Value Decomposition
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- Video of lecture Friday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember3.mp4?vrtx=view-as-webpage"
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- 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
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- Video of lecture Friday at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember3.mp4?vrtx=view-as-webpage
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- Reading recommendations:
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- See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
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- HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1
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- CMB sections 1.1 and 3.1
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### Week 36 September 6-10
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- Lab Wednesday: Exercises 1 and 2 from week 36
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- Lecture Thursday: Summary from last week on SVD, Statistics, probability theory and linear regression
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- Video of Lecture https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember9.mp4?vrtx=view-as-webpage".
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- Video of Lecture https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember9.mp4?vrtx=view-as-webpage
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- Friday: Linear Regression and links with Statistics, Resampling methods and presentation of first project.
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- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember10.mp4?vrtx=view-as-webpage"
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- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember10.mp4?vrtx=view-as-webpage
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- Recommended Reading: Lectures on Regression, Bishop 1.1, 1.2, 2.1, 2.2, 2.3 and 3.1, Hastie et al chapter 3
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- Reading recommendations:
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- Lectures on Regression for week 36 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
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- Bishop 1.1, 1.2, 2.1, 2.2, 2.3 and 3.1
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- Hastie et al chapter 3
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### Week 37 September 13-17
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- Lab Wednesday:
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- Lab Wednesday: Work on Project 1
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- Lecture Thursday: Resampling methods, cross-validation and Bootstrap
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- Lecture Friday: More on Resampling methods and summary of linear regression
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- Reading recommendations:
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- Recommended Reading:
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- Lectures on Resampling methods for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
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- Bishop 1.3 (cross-validation) and 3.2 (bias-variance tradeoff)
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- 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.
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- Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) and 7.11 (bootstrap)
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- Goodfellow et al discuss some of these topics in sections 5.2-5.5.
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### Week 38 September 20-24
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- Lab Wednesday:
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- Lab Wednesday: Work on Project 1
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- Lecture Thursday: Classification problems and Logistic Regression, from binary cases to several categories
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- Lecture Friday: Logistic Regression and gradient optimization
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- Reading recommendations: See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
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- Reading recommendations:
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- See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
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- Chapter
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### Week 39 September 27- October 1
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- Lab Wednesday:
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- Lab Wednesday: Work on Project 1
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- Lecture Thursday: Gradient Optimization methods
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- Lecture Friday: Deep Learning and Neural Networks
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- Reading recommendations: See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
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- Chapter
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- Reading recommendations:
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- See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
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### Week 40 October 4-8
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- Lab Wednesday:
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- Lab Wednesday: Wrap up project 1 and start project 2
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- Lecture Thursday: Writing a feed-forward Neural Network code for regression and classification
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- Lecture Friday: Deep Learning and TensorFlow and Keras
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- Reading recommendations:
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- Chapter
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### Week 41 October 11-15
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- Lab Wednesday:
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- Lecture Thursday: Deep learning and Neural Networks
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@@ -88,43 +105,43 @@ For the reading assignments we use the following abbreviations:
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- Lecture Thursday: Convolutional Neural Networks and classification problems
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- Lecture Friday: Convolutional Neural Networks and classification problems
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- Reading recommendations:
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- Chapter
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### Week 43 October 25-29
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- Lab Wednesday:
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- Lecture Thursday: Recurrent Neural Networks
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- Lecture Friday: Recurrent Neural Networks and time series
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- Reading recommendations:
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- Chapter
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### Week 44 November 1-5
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- Lab Wednesday:
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- Lecture Thursday: Decision trees, classification and regression
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- Lecture Friday: Decision trees, basic algorithms
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- Reading recommendations:
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- Chapter
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### Week 45 November 8-12
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- Lab Wednesday:
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- Lecture Thursday: Ensemble methods, bagging and random forests
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- Lecture Friday: Boosting and gradient boosting
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- Reading recommendations:
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- Chapter
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### Week 46 November 15-19
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- Lab Wednesday:
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- Lecture Thursday:
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- Lecture Friday: Unsupervised Learning, k-means
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- Reading recommendations:
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- Chapter
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### Week 47 November 22-26
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- Lab Wednesday:
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- Lecture Thursday: Unsupervised Learning, Principal Component Analysis (PCA)
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- Lecture Friday: Unsupervised Learning and PCA and Clustering
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- Reading recommendations:
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- Chapter
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### Week 48 November 29- December 2
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- Lab Wednesday:
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- Lecture Thursday: Unsupervised Learning and Clustering
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- Lecture Friday: Summary of course
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- Reading recommendations:
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- Chapter
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