Tentative schedule for FYS-STK3155/4155

Morten Hjorth-Jensen [1, 2]

[1] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University, USA [2] Department of Physics, University of Oslo, Norway

Teaching schedule Fall 2018

Acronyms for textbooks and references to chapter

HTF: Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer
AG: Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly

Lectures are Thursday and Fridays 1215pm-2pm. The computer lab is (projects and exercises) is on Wednesdays. Depending on how many people register for the course, we may set up four lab groups as follows

Group 1: Wednesdays 10am-12pm
Group 2: Wednesdays 12pm-2pm
Group 3: Wednesdays 2pm-4pm
Group 4: Wednesdays 4pm-6pm

Week	Topics to be covered	Projects, exercises and deadlines	Reading assignments	Lab activities
Week 34	Introduction and regression analysis	Exercises TBD	HTF chapters 1-3 and lecture notes	No lab first week
Week 35	Regression analysis	Exercises TBD	HTF chapter 3 and lecture notes	Introduction to Git, GitHub and Python software, Python technicalities and work on exercises
Week 36	Regression analysis and nearest neighbors	Exercises TBD	HTF chapters 3, 4 and 13 and lecture notes	Work on exercises
Week 37	Classification and logistic regression	Presentation of Project 1, deadline October 1	HTF chapter 4 and lecture notes	Work on project 1
Week 38	Optimization methods	Exercises and project 1	HTF chapter 5 and lecture notes	Work on project 1, deadline October 1
Week 39	Statistics, Bayesian statistics	Project 1	Lecture notes	Work on Project 1
Week 40	Statistics, Monte Carlo and Randow walks	Presentation of project 2, deadline November 5	Lecture notes	Deadline project 1, October 1
Week 41	Statistics, Monte Carlo, Gibbs and Metropolis sampling	Project 2	Lecture notes	Work on project 2
Week 42	Neural networks	Project 2	HTF chapter 11 and lecture notes	Work on project 2
Week 43	Neural networks	Project 2	HTF chapter 11 and lecture notes	Work on project 2
Week 44	Neural networks	Project 2	HTF chapter 11 and lecture notes	Work on project 2
Week 45	Support Vector Machines	Presentation and discussion of project 3	HTF chapter 12 and lecture notes	Deadline project 2 November 5
Week 46	Decision trees	Project 3	HTF chapter 9 and lecture notes	Work on project 3
Week 47	Unsupervised learning, Boltzmann machines	Project 3	HTF chapter 14 and lecture notes	Work on project 3
Week 48	Unsupervised learning, summary of course and final workshop	Project 3	Lecture notes	Final workshop with presentation of project 3