    Course module   SOWMKI75  Category   MA (Master)  Language of instruction   English  Offered by   Radboud University; Faculty of Social Sciences; Artificial Intelligence;  Lecturer(s)     Academic year   2022   Period   SEM2  (30/01/2023 to 14/07/2023) 
 Starting block   SEM2  
 Course mode   fulltime  
 Remarks     Registration using OSIRIS   Yes  Course open to students from other faculties   Yes  Preregistration   No  Waiting list   No  Placement procedure    
     
Upon successful completion of the course, the student can:
 Understand the mathematics behind machine learning algorithms
 Understand the mathematics behind deep learning algorithms
 Deploy deep learning models in production
 Understand and design machine learning procedures algorithms and pipelines
 Have an understanding of the intrinsic link between probability and statistical machine learning


Introduction to optimization techniques, Gradient descent, Model Selection, BiasVariance Tradeoff, Gaussian Processes, Bagging, Boosting, Decision Trees. An introduction to data structure optimization, Stochastic Gradient descent and all its variants, batch learning. Automatic differentiation computational graphs and learning optimization , Deep learning, neural networks and backpropagation.

 
Essential knowledge to be able to pass the course::
 Probability theory, calculus and linear algebra
 Basic Python programming
 Basic understanding of computational complexity
 Basic knowledge of a Unix/Linux operating system
Advised:
 Measure theory
 Stochastic calculus
 Advanced programming and mathematical logic knowledge


The test will exist of four larger individual assignments
There will be one course grade which is the average of the assignments
Possibility to resit of each type: Assignments can be handed in again for grading if and only if the original grade was lower than 5.5
Grading for each part is registered in Brightspace, only the final grade is published in Osiris.


The reading material is nonobligatory where students can go and look for extra material or deeper understanding.




   Recommended materialsBookThe Elements of Statistical Learning  available pdf https://web.stanford.edu/~hastie/ElemStatLearn/ 
 BookAll of Statistics: A Concise Course in Statistical Inference 
ISBN  :   9781441923226 
Author  :   Larry A. Wasserman 
 BookISBN  :   9780195124415 
Title  :   Complexity: A Guided Tour 
Author  :   Melanie Mitchell 
 BookISBN  :   9780262039406 
Title  :   Foundations of Machine Learning 
Author  :   Afshin Rostamizadeh, Ameet Talwalkar, and Mehryar Mohri 
 BookISBN  :   9781449369415 
Title  :   Introduction to machine learning with python 
Author  :   Mueller, Guido 
 BookISBN  :   9781461468486 
Title  :   Applied predictive modeling 
Author  :   Kuhn, Johnson 
 BookISBN  :   9780262035613 
Title  :   Deep Learning 
Author  :   Goodfellow, Bengio, Courville 
 BookISBN  :   9780241398630 
Title  :   The Art of Statistics: How to Learn from Data 
Author  :   Spiegelhalter 


Instructional modesTestsExamTest weight   1 
Test type   Exam 
Opportunities   Block SEM2, Block SEM2 


  
 
 