Subject: Subject Sylbus: Optimization Methods in Machine Learning - 096336 (Current)

Optimization Methods in Machine Learning - 096336
Will be learned this year
Credit
Points
2.0
 
  Lecture Exercise Laboratory Project or
Seminar
House
Work
Weekly
Hours
2       4

Determination of the grade according to progress during the semester and the submission of the final thesis


Prerequisites: Probability (Ie) 094411
or Introduction to Probability H 104034
or Probability Theory 104222
or Probability (Advanced) 094412


Theory of Efficient Optimization Algorithms for Continuous Big Data Analytics. Subjects Will Include: First-Order Methods for Convex and Non-Convex Optimization, Stochastic Optimization Methods for Convex and Non-Convex Problems and Efficient Algorithms for Efficient Algorithms with Rigorous Proofs of Their Computational Efficiency, as Well as the Development of Complementary Lower Bounds. Online at the End of the Course the Student Will Know:
1. to Understand the Principles, and to Implememt a Large Variety of Important and Central Optimization Algorithms in the Field of Machine
2. to Indenpendently Read and Understand Contemporary Litreature in
3. to Embark on Academic Reasearch (Both Theoretical and Practical) in the Field.


Timetable to semester 02/2020 2020/2021 Spring Semester
RoomBuildingHourdayLecturerExercise
Lecture
no.Registering
Group
  08:30-10:30WednesdayDoctor Garber DanLecture1010


Textbooks
PublishedPublisherAuthorsBook
2015now publisherssebastian bubeckconvex optimization algorithms and complexity
2016now publisherselad hazanintroduction to online convex optimization

Created in 06/03/2021 Time 01:43:59