diff --git a/doc/LectureNotes/schedule.md b/doc/LectureNotes/schedule.md index abbb326d5..9a54ff154 100644 --- a/doc/LectureNotes/schedule.md +++ b/doc/LectureNotes/schedule.md @@ -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