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@@ -41,7 +41,7 @@ We recommend also refreshing your knowledge on Statistics and Probability theory
### Statistical analysis and optimization of data
The following topics will be covered
The following topics are normally be covered
- Basic concepts, expectation values, variance, covariance, correlation functions and errors;
- Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
- Central elements of Bayesian statistics and modeling;
@@ -52,7 +52,7 @@ The following topics will be covered
### Machine learning
The following topics will be covered:
The following topics are typically covered:
- Linear Regression and Logistic Regression;
- Neural networks and deep learning, including convolutional and recurrent neural networks
- Decisions trees, Random Forests, Bagging and Boosting
@@ -77,18 +77,21 @@ the theory and various algorithms discussed during the lectures. We recommend st
## Teaching Assistants FS21
* Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no
* Stian Bilek, stian.bilek@fys.uio.no
* Linus Ekstrøm, linueks@gmail.com, linus.ekstrom@fys.uio.no
* Nicholas Karlsen, nicholaskarlsen1102@gmail.com, nicholas.karlsen@fys.uio.no
* Bendik Steinsvåg Dalen, b.s.dalen@fys.uio.no
* Philip Karim Sørli Niane, p.k.s.niane@fys.uio.no
* Bennosh Ashrafi, behnoosh.ashrafi@fys.uio.no
* Frida Marie Engøy Westbye, f.m.e.westby@fys.uio.no
* Ida Torkjellsdatter Storehaug, i.t.storehaug@fys.uio.no
* Joao Guilherme Carvalho Inacio, joaogca@fys.uio.no
* Sigurd Sørlie Rustad, s.s.rustad@fys.uio.no
* Stian Dysthe Bilek stian.bilek@fys.uio.no
* Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no
## Practicalities
1. Four lectures per week, Fall semester, 10 ECTS. The lectures will be recorded and linked to this site and the official University of Oslo website for the course;
2. Two hours of laboratory sessions for work on computational projects and exercises for each group. Due to social distancing, at most 15 participants can attend. There will also be fully digital laboratory sessions for those who cannot attend;
2. Two hours of laboratory sessions for work on computational projects and exercises for each group. There will also be fully digital laboratory sessions for those who cannot attend;
3. Three projects which are graded and count 1/3 each of the final grade;
4. A selected number of weekly assignments;
5. The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;
@@ -221,200 +224,6 @@ The link here https://www.mn.uio.no/english/research/about/centre-focus/innovati
# Teaching schedule with links to material
This course will be delivered in a hybrid mode, with online lectures and on site or online laboratory sessions.
1. Four lectures per week, Fall semester, 10 ECTS. The lectures are in person but will be recorded and linked to this site and the official University of Oslo website for the course;
2. Two hours of laboratory sessions for work on computational projects and exercises for each group. There will also be fully digital laboratory sessions for those who cannot attend;
3. Three projects which are graded and count 1/3 each of the final grade;
4. A selected number of weekly assignments;
5. The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;
6. The course is offered as a FYS-MAT4155 (Master of Science level) and a FYS-MAT3155 (senior undergraduate) course;
7. Videos of teaching material are available via the links at https://compphysics.github.io/MachineLearning/doc/web/course.html;
8. Weekly emails with summary of activities will be mailed to all participants;
# Communication channels
- Chat and communications via canvas.uio.no, GDPR safe
- Slack channel: machinelearninguio.slack.com
- Piazza : enlist at https:piazza.com/uio.no/fall2021/fysstk4155
## Weekly Schedule
For the reading assignments we use the following abbreviations:
- GBC: Goodfellow, Bengio, and Courville, Deep Learning
- CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning
- HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning
- AG: Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow
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.
- 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.
- See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html
### 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
- CMB sections 1.1 and 3.1
### 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
- Reading recommendations:
- Lectures on Regression for week 36 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- 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: Work on Project 1
- Lecture Thursday: Resampling methods, cross-validation and Bootstrap
- Video of Lecture, first part at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember16Firstpart.mp4?vrtx=view-as-webpage"
- Video of Lecture, second part at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember16SecondPart.mp4?vrtx=view-as-webpage
- Lecture Friday: More on Resampling methods and summary of linear regression
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember17.mp4?vrtx=view-as-webpage
- Reading recommendations:
- 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)
- Goodfellow et al discuss some of these topics in sections 5.2-5.5.
### Week 38 September 20-24
- Lab Wednesday: Work on Project 1
- Lecture Thursday: Classification problems and Logistic Regression, from binary cases to several categories
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember23.mp4?vrtx=view-as-webpage
- Lecture Friday: Logistic Regression and start discussions of gradient optimization
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember24.mp4?vrtx=view-as-webpage
- Reading recommendations:
- See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Bishop 4.1, 4.2 and 4.3. Not all the material is relevant or will be covered. Section 4.3 is the most relevant, but 4.1 and 4.2 give interesting background readings for logistic regression
- Hastie et al 4.1, 4.2 and 4.3 on logistic regression
- For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.
### Week 39 September 27- October 1
- Lab Wednesday: Work on Project 1
- Lecture Thursday: Gradient Optimization methods
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember30.mp4?vrtx=view-as-webpage
- Lecture Friday: Gradient methods
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober1.mp4?vrtx=view-as-webpage
- Reading recommendations:
- See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.
### Week 40 October 4-8
- Lab Wednesday: Wrap up project 1
- Lecture Thursday: Stochastic gradient methods and start discussion of neural networks
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober7.mp4?vrtx=view-as-webpage
- Lecture Friday: Deep Learning and Neural Networks
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober8.mp4?vrtx=view-as-webpage
- Reading recommendations:
- See lecture notes for week 40 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- For neural networks we recommend Goodfellow et al chapters 6 and 7 and Bishop 5.1-5.4
### Week 41 October 11-15
- Lab Wednesday: Work on project 2
- Lecture Thursday: Deep learning and Neural Networks
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober14.mp4?vrtx=view-as-webpage
- Lecture Friday: Tensorflow and the mathematics of neural networks
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureOctober15.mp4?vrtx=view-as-webpage
- Reading recommendations:
- See lecture notes for week 41 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- For neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications. See also Aurelien Geron's chapters 10-11 at https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf.
### Week 42 October 18-22
- Lab Wednesday: Work on project 2
- Lecture Thursday: Solving differential equations with neural networks and start Convolutional Neural Networks and classification problems
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober21.mp4?vrtx=view-as-webpage
- Lecture Friday: Convolutional Neural Networks and classification problems
- Video of lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureOctober22.mp4?vrtx=view-as-webpage
- Reading recommendations:
- See lecture notes for week 42 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- For neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. See also chapter 11 and 12 on practicalities and applications
### Week 43 October 25-29
- 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 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 chapter 11 and Geron's text chapter 8
### 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
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureNovember5.mp4?vrtx=view-as-webpage
- 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. 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, project 3 available Friday 12th. Deadline project 2 is November 20.
- Lecture Thursday: Decision Trees and Ensemble methods, Bagging and Voting
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureNovember11.mp4?vrtx=view-as-webpage
- Lecture Friday: Ensemble Methods, Random Forests, Boosting and gradient boosting
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureNovember12.mp4?vrtx=view-as-webpage
- Reading recommendations:
- See lecture notes for week 45 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Decision Trees: Geron's chapter 6 covers decision trees while ensemble models, voting and bagging are discussed in chapter 7.
- See also lecture from "STK-IN4300, lecture 7":"https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf".
- Chapter 9.2 of Hastie et al contains also a good discussion.
### Week 46 November 15-19
- Lab Wednesday: Work on project 3
- Lecture Thursday: Support Vector machines. Summary Ensemble Methods, Random Forests, Boosting and gradient boosting
- Lecture Friday: Workshop on project 3
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureNovember19.mp4?vrtx=view-as-webpage
- Reading recommendations:
- See lecture notes for week 46 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Hastie et al chapter 12
- Bishop chapter 7.1 and 7.2
### Week 47 November 22-26
- Lab Wednesday: Work on project 3
- Lecture Thursday: Support Vector Machines
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureNovember25.mp4?vrtx=view-as-webpage
- Lecture Friday: Support Vector Machines and Summary of Course
- Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureNovember26.mp4?vrtx=view-as-webpage
- Reading recommendations:
- See lecture notes for week 47 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
- Geron's chapter 5.
- Hastie et al Chapter 12 (sections 12.1-12.3 are the most relevant ones)
- Bishop chapter 7, with sections 7.1 and 7.2 as the essential ones