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Coursebooks 2017-2018
Optimization for machine learning
CS-439
Lecturer(s) :
Jaggi MartinLanguage:
English
Summary
This course teaches an overview of modern optimization methods, for applications in machine learning and data science. In particular, scalability of algorithms to large datasets will be discussed in theory and in implementation.Content
This course teaches an overview of modern optimization methods, for applications in machine learning and data science. In particular, scalability of algorithms to large datasets will be discussed in theory and in implementation.
Basic Contents:
Convexity, Gradient Methods, Proximal algorithms, Stochastic and Online Variants of mentioned methods, Coordinate Descent Methods, Subgradient Methods, Frank-Wolfe, Accelerated Methods, Primal-Dual context and certificates, Lagrange and Fenchel Duality, Second-Order Methods, Quasi-Newton Methods. Black-Box Optimization.
Advanced Contents:
Parallel and Distributed Optimization Algorithms, Synchronous and Asynchronous Communication.
Computational and Statistical Trade-Offs (Time vs Data vs Accuracy). Variance Reduced Methods, and Lower Bounds.
Non-Convex Optimization: Convergence to Critical Points, Saddle-Point methods, Alternating minimization for matrix and tensor factorizations
Keywords
Optimization, Machine learning
Learning Prerequisites
Recommended courses
- CS-433 Machine Learning
Important concepts to start the course
- Previous coursework in calculus, linear algebra, and probability is required.
- Familiarity with optimization and/or machine learning is useful.
Learning Outcomes
By the end of the course, the student must be able to:- Assess / Evaluate the most important algorithms, function classes, and algorithm convergence guarantees
- Compose existing theoretical analysis with new aspects and algorithm variants.
- Formulate scalable and accurate implementations of the most important optimization algorithms for machine learning applications
- Characterize trade-offs between time, data and accuracy, for machine learning methods
Transversal skills
- Use both general and domain specific IT resources and tools
- Summarize an article or a technical report.
Teaching methods
- Lectures
- Exercises with Theory and Implementation Assignments
Expected student activities
Students are expected to:
- Attend the lectures and exercises
- Give a short scientific presentation about a research paper
- Read / watch the pertinent material
- Engage during the class, and discuss with other colleagues
Assessment methods
- Final Exam
Supervision
Office hours | Yes |
Assistants | Yes |
Forum | Yes |
Resources
Virtual desktop infrastructure (VDI)
No
Websites
In the programs
- SemesterSpring
- Exam formWritten
- Credits
4 - Subject examined
Optimization for machine learning - Lecture
2 Hour(s) per week x 14 weeks - Exercises
2 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
4 - Subject examined
Optimization for machine learning - Lecture
2 Hour(s) per week x 14 weeks - Exercises
2 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
4 - Subject examined
Optimization for machine learning - Lecture
2 Hour(s) per week x 14 weeks - Exercises
2 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
4 - Subject examined
Optimization for machine learning - Lecture
2 Hour(s) per week x 14 weeks - Exercises
2 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
4 - Subject examined
Optimization for machine learning - Lecture
2 Hour(s) per week x 14 weeks - Exercises
2 Hour(s) per week x 14 weeks
- Semester
- SemesterSpring
- Exam formWritten
- Credits
4 - Subject examined
Optimization for machine learning - Lecture
2 Hour(s) per week x 14 weeks - Exercises
2 Hour(s) per week x 14 weeks
- Semester
Reference week
Mo | Tu | We | Th | Fr | |
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8-9 | |||||
9-10 | |||||
10-11 | |||||
11-12 | |||||
12-13 | |||||
13-14 | CE2 | ||||
14-15 | |||||
15-16 | CE2 | ||||
16-17 | |||||
17-18 | |||||
18-19 | |||||
19-20 | |||||
20-21 | |||||
21-22 |
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- Autumn semester
- Winter sessions
- Spring semester
- Summer sessions
- Lecture in French
- Lecture in English
- Lecture in German