machine learning: fundamentals and algorithms

Master in Computer Science Machine Learning: Fundamentals and Algorithms Course level: Master [M1] Track(s): [MLDM, DSC] ECTS Credits: 4 Course instructors: [Ievgen Redko, Amaury Habrard] Education period: [2nd] semester Language of instruction: English Expected prior-knowledge: [Data Analysis, Optimization and Operation Research] Aim and learning outcomes: This course introduces fundamental concepts in machine learning and presents some classical approaches and algorithms. The scikit-learn library is presented during the practical sessions. The course aims at providing fundamental basics for using machine learning techniques. Keywords: [Machine Learning, SVM, Decision Trees, Deep Learning, HMM] Syllabus: - Decision trees and Random Forests - Hidden Markov Model - Introduction to Support Vector Machines - Neural Networks and Deep Learning - Practical: Introduction to Scikit-learn Organisation and timetable: [Volume CM/TD/TP] Lectures (10h), tutorials (10h) and lab sessions (10h). Form(s) of Assessment: 1 theoretical examination (2h, 2/3), 1/3 practical assignments. Literature and study materials: - An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Nello Cristianini, John Shawe-Taylor, Cambridge University Press 2010: I-XIII, 1-189 - Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer 2006 Additional information/Contacts: [email protected] [email protected]

Upload: others

Post on 19-Dec-2021

12 views

Category:

Documents

0 download

Report

Download

Embed Size (px):

TRANSCRIPT

Page 1: Machine Learning: Fundamentals and Algorithms

Master in Computer Science

Machine Learning: Fundamentals and Algorithms Course level: Master [M1]

Track(s): [MLDM, DSC]

ECTS Credits: 4

Course instructors: [Ievgen Redko, Amaury Habrard]

Education period: [2nd] semester Language of instruction: English

Expected prior-knowledge: [Data Analysis, Optimization and Operation Research]

Aim and learning outcomes: This course introduces fundamental concepts in machine learning and presents some classical approaches and algorithms. The scikit-learn library is presented during the practical sessions. The course aims at providing fundamental basics for using machine learning techniques.

Keywords: [Machine Learning, SVM, Decision Trees, Deep Learning, HMM]

Syllabus:

- Decision trees and Random Forests

- Hidden Markov Model

- Introduction to Support Vector Machines

- Neural Networks and Deep Learning

- Practical: Introduction to Scikit-learn

Organisation and timetable: [Volume CM/TD/TP] Lectures (10h), tutorials (10h) and lab sessions (10h).

Form(s) of Assessment: 1 theoretical examination (2h, 2/3), 1/3 practical assignments.

Literature and study materials:

- An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Nello Cristianini, John Shawe-Taylor, Cambridge University Press 2010: I-XIII, 1-189 - Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer 2006

Additional information/Contacts:

[email protected]

Ch8 DC Machine Fundamentals

Fundamentals of Machine Design - dbc.wroc.pl of Machine Design...Fundamentals of Machine Design - dbc.wroc.pl

FUNDAMENTALS OF MACHINE LEARNING FOR …machinelearningbook.com/wp-content/uploads/2015/07/... · PREDICTIVE DATA ANALYTICS Algorithms, Worked Examples, and ... Learning for Predictive

Fundamentals of Cryptography: Algorithms, and Security

Stochastic Optimization Algorithms for Machine Learninglaurent.risser.free.fr/TMP_SHARE/optimCIMI2018/slidesGuanghuiLan.pdf · Stochastic Optimization Algorithms for Machine Learning

Fundamentals of Machine Vision

Fundamentals of Machine Learning

Comparative Study on Machine Learning Algorithms using …...12. Symbolic machine learning algorithms 13. Subsymbolic machine learning algorithms. Decision tree learning:Decision tree

Fundamentals of Cryptography: Algorithms, and Security Services

Fundamentals, Terminology, Traversal, Algorithms Graph Algorithms Telerik Algo Academy