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@@ -22,91 +22,99 @@ For the reading assignments we use the following abbreviations:
- HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning
- AG: Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow
### Week 35 August 23-27
### 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
- 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 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html
- 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 36 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
- Reading recommendations: See lecture notes for week 36 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 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".
- 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 37 September 6-10
- Lab Wednesday:
- Lecture Thursday: Statistical interpretation of Linear Regression
- Lecture Friday: Bias-Variance tradeoff
- Reading recommendations: See lecture notes for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html. GBC sections 5.2-5.5, CMB section 3.2
- Chapter
### Week 38 September 13-17
### 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".
- 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 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Reading recommendations: See lecture notes for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Chapter
### Week 39 September 20-24
### Week 38 September 20-24
- Lab Wednesday:
- 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 39 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 40 September 27- October 1
### Week 39 September 27- October 1
- Lab Wednesday:
- Lecture Thursday: Gradient Optimization methods
- Lecture Friday: Deep Learning and Neural Networks
- Reading recommendations: See lecture notes for week 40 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Reading recommendations: See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Chapter
### Week 41 October 4-8
### Week 40 October 4-8
- Lab Wednesday:
- 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 42 October 11-15
### Week 41 October 11-15
- Lab Wednesday:
- Lecture Thursday: Deep learning and Neural Networks
- Lecture Friday: Convolutional Neural Networks, basic elements
- Reading recommendations:
- GoodFellow et al, Chapter 9
### Week 43 October 18-22
### Week 42 October 18-22
- Lab Wednesday:
- Lecture Thursday: Convolutional Neural Networks and classification problems
- Lecture Friday: Convolutional Neural Networks and classification problems
- Reading recommendations:
- Chapter
### Week 44 October 25-29
### Week 43 October 25-29
- Lab Wednesday:
- Lecture Thursday: Recurrent Neural Networks
- Lecture Friday: Recurrent Neural Networks and time series
- Reading recommendations:
- Chapter
### Week 45 November 1-5
### Week 44 November 1-5
- Lab Wednesday:
- Lecture Thursday: Decision trees, classification and regression
- Lecture Friday: Decision trees, basic algorithms
- Reading recommendations:
- Chapter
### Week 46 November 8-12
### 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 47 November 15-19
### Week 46 November 15-19
- Lab Wednesday:
- Lecture Thursday:
- Lecture Friday: Unsupervised Learning, k-means
- Reading recommendations:
- Chapter
### Week 48 November 22-26
### 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 49 November 29- December 2
### Week 48 November 29- December 2
- Lab Wednesday:
- Lecture Thursday: Unsupervised Learning and Clustering
- Lecture Friday: Summary of course