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update first week

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Morten Hjorth-Jensen
2023-05-28 21:57:39 +02:00
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@@ -7,9 +7,14 @@ DATE: Week 34, August 21-25, 2021
!split
===== Overview of first week =====
The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures in a flipped mode (promoting active learning) and work on exercices and projects.
The sessions will begin with lectures and questions and answers about the material to be covered every week. There are four groups, Tuesdays 815am-12pm and 1215pm-4pm and
Wednesdays 815am-12pm and 1215pm-4pm. Please sign up as soon as possible for one of the groups. Max capacity per group is 30-40 persons.
The sessions on Tuesdays and Wednesdays last four hours and will
include partly lectures in a flipped mode (promoting active learning)
and work on exercices and projects. The sessions will begin with
lectures and questions and answers about the material to be covered
every week. There are four groups, Tuesdays 815am-12pm and 1215pm-4pm
and Wednesdays 815am-12pm and 1215pm-4pm. Please sign up as soon as
possible for one of the groups. Max capacity per group is 30-40
persons.
On Thursdays we have a regular lecture. These lectures start at 1215pm and end at 2pm.
The first week we wtart with simple linear regression, a repetition of linear algebra and elements of statistics needed for the course.
@@ -31,7 +36,7 @@ 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
Reading recommendations this week: 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
Reading recommendations this week: 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 (these notes).
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@@ -85,7 +90,9 @@ _Teachers :_
* _Office hours_: *Anytime*! Individual or group office hours can be arranged either in person or via zoom. Feel free to send an email for planning.
* Ida Torkjellsdatter Storehaug, i.t.storehaug@fys.uio.no
* Fahimeh Najafi,
* Mia K.O. Kvalsund,
* Karl Henrik Fredly,
!eblock
@@ -95,9 +102,9 @@ _Teachers :_
!bblock
o Project 1: October 11 (available September 5) graded with feedback)
o Project 2: November 11 (available October 7, graded with feedback)
o Project 3: December 9 (available November 11, graded with feedback)
o Project 1: October 9 (available September 4) graded with feedback)
o Project 2: November 6 (available October 6, graded with feedback)
o Project 3: December 11 (available November 10, graded with feedback)
Projects are handed in using _Canvas_. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via _Canvas_.
@@ -197,17 +204,24 @@ We plan to cover the following topics:
!split
===== Topics covered in this course: Machine Learning =====
===== Topics covered in this course =====
!bblock
The following topics will be covered
* Linear Regression and Logistic Regression;
* Neural networks and deep learning, including convolutional and recurrent neural networks;
* Decisions trees, Random Forests, Bagging and Boosting;
* Support vector machines;
* Bayesian linear and logistic regression (tentative);
* Boltzmann Machines (tentative);
* Unsupervised learning Dimensionality reduction, from PCA to clustering;
* Pre deep-learning revolution (2008 approx)
* Linear Regression and Logistic Regression, classification and regression problems;
* Bayesian linear and logistic regression, kernel regression;
* Decisions trees, Random Forests, Bagging and Boosting methods;
* Support vector machines (only survey);
* Unsupervised learning and dimensionality reduction, from PCA to clustering;
* Deep learning
* Neural networks and deep learning;
* Convolutional neural networks;
* Recurrent neural networks;
* Autoencoders
* Generative methods with an emphasis on Boltzmann Machines, Variational Autoencoders and Generalized Adversarial Networks;
Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
@@ -230,21 +244,18 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand
===== Other courses on Data science and Machine Learning at UiO =====
The link here URL:"https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" gives an excellent overview of courses on Machine learning at UiO.
o "FYS5419 Quantum Computing and Quantum Machine Learning":"https://www.uio.no/studier/emner/matnat/fys/FYS5419/index-eng.html"
o "FYS5429 Advanced Machine Learning for the Physical Sciences":"https://www.uio.no/studier/emner/matnat/fys/FYS5429/index-eng.html"
o "STK2100 Machine learning and statistical methods for prediction and classification":"http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html".
o "IN3050/4050 Introduction to Artificial Intelligence and Machine Learning":"https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html". Introductory course in machine learning and AI with an algorithmic approach.
o "STK-INF3000/4000 Selected Topics in Data Science":"http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html". The course provides insight into selected contemporary relevant topics within Data Science.
o "IN4080 Natural Language Processing":"https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html". Probabilistic and machine learning techniques applied to natural language processing.
o "STK-IN4300 Statistical learning methods in Data Science":"https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html". An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.
o "INF4490 Biologically Inspired Computing":"http://www.uio.no/studier/emner/matnat/ifi/INF4490/". An introduction to self-adapting methods also called artificial intelligence or machine learning.
o "IN-STK5000 Adaptive Methods for Data-Based Decision Making":"https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html". Methods for adaptive collection and processing of data based on machine learning techniques.
o "IN5400/INF5860 Machine Learning for Image Analysis":"https://www.uio.no/studier/emner/matnat/ifi/IN5400/". An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
o "TEK5040 Deep learning for autonomous systems":"https://www.uio.no/studier/emner/matnat/its/TEK5040/". The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.
o "IN3310/4310 Deep Learnig for Image Analysis":"https://www.uio.no/studier/emner/matnat/ifi/IN4310/index.html"
o "STK4051 Computational Statistics":"https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html"
o "STK4021 Applied Bayesian Analysis and Numerical Methods":"https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html"
!split
===== Introduction =====