diff --git a/doc/web/course.do.txt b/doc/web/course.do.txt index 8098f41b9..4ba3df293 100644 --- a/doc/web/course.do.txt +++ b/doc/web/course.do.txt @@ -213,7 +213,7 @@ Acronyms for textbooks and references to chapter |----------------------------------------------------------------------------------------------------------------------------| | Week and days | Topics to be covered | Projects and deadlines | Reading assignments| Lab activities | |----------------------------------------------------------------------------------------------------------------------------| -| Week 34| Introduction and Regressions analysis | | | | +| Week 34| Introduction and regression analysis | | | | |----------------------------------------------------------------------------------------------------------------------------| | Week 35 | Regression analysis | | | | |----------------------------------------------------------------------------------------------------------------------------| @@ -221,25 +221,25 @@ Acronyms for textbooks and references to chapter |----------------------------------------------------------------------------------------------------------------------------| | Week 37 | Classification and logistic regression | | | | |----------------------------------------------------------------------------------------------------------------------------| -| Week 38 | Statistics, Monte Carlo and Randow walks | | | | +| Week 38 | Optimization methods | | | | |----------------------------------------------------------------------------------------------------------------------------| -| Week 39 | Statistics, Monte Carlo and Randow walks | | | | +| Week 39 | Statistics, Bayesian statistics | | | | |----------------------------------------------------------------------------------------------------------------------------| | Week 40 | Statistics, Monte Carlo and Randow walks | | | | |----------------------------------------------------------------------------------------------------------------------------| -| Week 41 | Neural Networks | | | | +| Week 41 | Statistics, Monte Carlo, Gibbs and Metropolis sampling | | | | |----------------------------------------------------------------------------------------------------------------------------| -| Week 42 | Neural Networks | | | | +| Week 42 | Neural networks | | | | |----------------------------------------------------------------------------------------------------------------------------| -| Week 43 | Neural Nteworks | | | | +| Week 43 | Neural networks | | | | |----------------------------------------------------------------------------------------------------------------------------| -| Week 44 | | | | | +| Week 44 | Neural networks | | | | |----------------------------------------------------------------------------------------------------------------------------| -| Week 45 | | | | | +| Week 45 | Support Vector Machines | | | | |----------------------------------------------------------------------------------------------------------------------------| -| Week 46 | | | | | +| Week 46 | Decision trees | | | | |----------------------------------------------------------------------------------------------------------------------------| -| Week 47 | | | | | +| Week 47 | Unsupervised learning, Boltzmann machines | | | | |----------------------------------------------------------------------------------------------------------------------------| -| Week 48 | | | | | +| Week 48 | Unsupervised learning and summary of course | | | | |----------------------------------------------------------------------------------------------------------------------------| diff --git a/doc/web/course.html b/doc/web/course.html index 8d094ae05..e85274b41 100644 --- a/doc/web/course.html +++ b/doc/web/course.html @@ -697,26 +697,33 @@ All the above topics will be supported by examples, hands-on exercises and proje

Teaching schedule Fall 2018

+Acronyms for textbooks and references to chapter + +

+ - + - - - - - - - - - - - - - - - + + + + + + + + + + + + + + +
Week and days Topics to be covered Projects and deadlines Reading assignments Lab activities
Week and days Topics to be covered Projects and deadlines Reading assignments Lab activities
Week 34 Introduction and Regressions analysis
Week 35
Week 36
Week 37
Week 38
Week 39
Week 40
Week 41
Week 42
Week 43
Week 44
Week 45
Week 46
Week 47
Week 48
Week 34 Introduction and regression analysis
Week 35 Regression analysis
Week 36 Regression analysis and nearest neighbors
Week 37 Classification and logistic regression
Week 38 Optimization methods
Week 39 Statistics, Bayesian statistics
Week 40 Statistics, Monte Carlo and Randow walks
Week 41 Statistics, Monte Carlo, Gibbs and Metropolis sampling
Week 42 Neural networks
Week 43 Neural networks
Week 44 Neural networks
Week 45 Support Vector Machines
Week 46 Decision trees
Week 47 Unsupervised learning, Boltzmann machines
Week 48 Unsupervised learning and summary of course