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Syllabus ( ELEC 642 )


   Basic information
Course title: Machine Learning for Signal Processing
Course code: ELEC 642
Lecturer: Prof. Dr. Koray KAYABOL
ECTS credits: 7.5
GTU credits: 3 (3+0+0)
Year, Semester: 1/2, Fall
Level of course: Second Cycle (Master's)
Type of course: Area Elective
Language of instruction: English
Mode of delivery: Face to face
Pre- and co-requisites: None
Professional practice: No
Purpose of the course: Grasping the fundamental machine learning methods for the problems encountered in digital signal processing and digital communications.
   Learning outcomes Up

Upon successful completion of this course, students will be able to:

  1. Apply the fundamental estimation methods for sequential data..

    Contribution to Program Outcomes

    1. Define and manipulate advanced concepts of Electronics Engineering
    2. Formulate and solve advanced engineering problems

    Method of assessment

    1. Written exam
    2. Homework assignment
  2. Apply the fundamental classification methods to sequential data.

    Contribution to Program Outcomes

    1. Define and manipulate advanced concepts of Electronics Engineering
    2. Formulate and solve advanced engineering problems
    3. Design and conduct research projects independently

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Term paper
  3. Apply the fundamental parameter estimation methods to dynamical systems.

    Contribution to Program Outcomes

    1. Define and manipulate advanced concepts of Electronics Engineering
    2. Formulate and solve advanced engineering problems

    Method of assessment

    1. Written exam
    2. Homework assignment
   Contents Up
Week 1: Introduction, probability and random Variables
Week 2: Random vectors and random processes
Week 3: Introduction to Estimation theory
Week 4: Adaptive filters
Week 5: Sequential Bayesian filtering: Kalman and particle filters
Week 6: Supervised signal classification: Generative and discriminative models
Week 7: Clustering: Expectation-maximization, k-means, model order selection
Week 8: Dimension reduction: Principle component analysis, independent component analysis
Week 9: Midterm exam, Graphical models
Week 10: Markov chains
Week 11: Hidden Markov models (HMM)
Week 12: Inference and learning in HMM: Viterbi and Baum-Welch algorithms
Week 13: Artificial neural networks
Week 14: Introduction to Deep Learning
Week 15*: -
Week 16*: Final exam
Textbooks and materials: Sergios Theodoridis, "Machine Learning: A Bayesian and Optimization Perspective", 1st Ed., Academic Press, 2015.

Christopher M. Bishop, "Pattern Recognition and Machine Learning", 2nd Ed., Springer, 2011.
Recommended readings: Ethem Alpaydın, "Introduction to Machine Learning", 2nd Ed., The MIT Press, 2010.
  * Between 15th and 16th weeks is there a free week for students to prepare for final exam.
Assessment Up
Method of assessment Week number Weight (%)
Mid-terms: 9 20
Other in-term studies: 0
Project: 9,10,11,12,13,14 30
Homework: 2, 4, 6 25
Quiz: 0
Final exam: 16 25
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 3 14
Own studies outside class: 3 14
Practice, Recitation: 0 0
Homework: 5 6
Term project: 8 6
Term project presentation: 0 0
Quiz: 0 0
Own study for mid-term exam: 10 1
Mid-term: 2.5 1
Personal studies for final exam: 10 1
Final exam: 3 1
    Total workload:
    Total ECTS credits:
*
  * ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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