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      Department of Industrial Engineering

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      CE 344 | Course Introduction and Application Information

      Course Name
      Advanced Automatic Learning
      Code
      Semester
      Theory
      (hour/week)
      Application/Lab
      (hour/week)
      Local Credits
      ECTS
      CE 344
      SPRING
      3
      0
      3
      5

      Prerequisites None
      Course Language English
      Course Type ELECTIVE_COURSE
      Course Level First Cycle
      Mode of Delivery
      Teaching Methods and Techniques of the Course Discussion
      Problem solving
      Question & Answer
      Criticism
      Lecture/Presentation
      National Occupational Classification Code -
      Course Coordinator
      • Dr. Öğr. Üyesi Kutluhan Erol
      Course Lecturer(s)
      • Dr. Öğr. Üyesi Kutluhan Erol
      Assistant(s) -
      Course Objectives The aim of this course is to provide an advanced knowledge of algorithms and techniques in the field of modern machine learning. Both the basic and advanced theoretical aspects of these algorithms and techniques, as well as the practical applications resulting from this theory will be discussed.
      Learning Outcomes The students who succeeded in this course;
      Name Description PC Sub * Contribution Level
      1 2 3 4 5
      LO1 Describe a wide range of advanced machine learning algorithms and techniques, 1.5 X
      LO2 Be able to apply algorithms and techniques in the field of advanced machine learning, 4 X
      LO3 Compare different algorithms and techniques in the field of advanced machine learning, 2 X
      LO4 Design various advanced machine learning algorithms for specific problems, 3.1 X
      LO5 Evaluate practical applications of advanced machine learning techniques. 5.2 X
      Course Description This course covers advanced machine learning topics, including collecting training data, learning to infer statistical structure from data, overfitting, parametric models and parameter selection, validation, regression, classification, nonparametric models, and clustering.
      Related Sustainable Development Goals
      -

       



      Course Category

      Core Courses
      Major Area Courses
      X
      Supportive Courses
      Media and Managment Skills Courses
      Transferable Skill Courses

       

      WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

      Week Subjects Required Materials Learning Outcome
      1 Introduction to machine learning. Probability repetition Part 1-2. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 e219e649
      2 Generative models for discrete data. Gaussian models Part 3-4. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 e219e649
      3 Bayesian and frequentist statistics Part 5-6. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 e219e649
      4 Linear and logistic regression Part 7-8. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 896e6dff
      5 Generalized linear models and exponential families Part 9. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 896e6dff
      6 Graphical models: Markov random field and Bayesian networks Part 10-19. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 14601732
      7 Mixture models and EM algorithm Part 11. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 14601732
      8 Hidden linear and sparse linear models Part 12-13. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 14601732
      9 Markov and hidden Markov models Part 17. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 1b81476f
      10 Exact inference for graphical models. Variational inference Part 20-21-22. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 c282d781
      11 Exact inference for graphical models. Variational inference Part 20-21-22. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 c282d781
      12 Monte Carlo and Markov chain Monte Carlo inference Part 23-24. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 1b81476f
      13 Core models Part 14. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 e219e649
      14 Grouping Part 25. Machine Learning: A Probabilistic Perspective. K. Murphy. ISBN: 9780262018029 c282d781
      15 Review of the period 1b81476f
      16 Final Exam 1b81476f

       

      Course Notes/Textbooks Kevin Murphy; Machine Learning: A Probabilistic Perspective; MIT Press; 2012; ISBN: 9780262018029
      Suggested Readings/Materials Christopher M. Bishop; Pattern Recognition and Machine Learning; Springer; 2006; ISBN: 9780387310732

       

      EVALUATION SYSTEM

      Semester Activities Number Weighting LO1 LO2 LO3 LO4 LO5
      Quizzes / Studio Critiques 4 12 X X
      Homework / Assignments 2 10 X X
      Project 1 40 X X X X
      Final Exam 1 38 X X
      Total 8 100

       

      ECTS / WORKLOAD TABLE

      Semester Activities Number Duration (Hours) Workload
      Participation - - -
      Theoretical Course Hours 16 3 48
      Laboratory / Application Hours - - -
      Study Hours Out of Class 14 2 28
      Field Work - - -
      Quizzes / Studio Critiques 4 2 8
      Portfolio - - -
      Homework / Assignments 2 5 10
      Presentation / Jury - - -
      Project 1 30 30
      Seminar / Workshop - - -
      Oral Exams - - -
      Midterms - - -
      Final Exam 1 26 26
          Total 150

       

      COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

      # PC Sub Program Competencies/Outcomes * Contribution Level
      1 2 3 4 5
      1

      Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computation, and related engineering discipline-specific topics; the ability to apply this knowledge to solve complex engineering problems.

      1

      Mathematics

      2

      Science

      3

      Basic Engineering

      4

      Computation

      5

      related engineering discipline-specific topics

      LO1
      6

      the ability to apply this knowledge to solve complex engineering problems.

      2

      Problem Analysis: Ability to identify, formulate and analyze complex engineering problems using basic knowledge of science, mathematics and engineering, and considering the UN Sustainable Development Goals relevant to the problem being addressed.

      LO3
      3

      Engineering Design: The ability to devise creative solutions to complex engineering problems; the ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions.

      1

      Ability to design creative solutions to complex engineering problems.

      LO4
      2

      Ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions.

      4

      Use of Techniques and Tools: Ability to select and use appropriate tectıniques, resources, and modern engineering and computing tools. including estimation and modeling. far the analysis and solution of complex engineering problems while recognizing their limitations.

      LO2
      5

      Research and ınvestigation: Ability to use research methods ta investigate complex engineering problems, including literature research, designing and conducting experiments, collecting data, and analyzing and interpreting results.

      1

      Literature research far the study of complex engineering problems

      2

      Designing experiments

      LO5
      3

      Ability to use research methods, including conducting experiments, collecting data. analyzing and interpreting results

      6

      Global lmpact of Engineering Practices: Knowledge of the impacts of engineering practices on s.ociety, health and safety. ttıe economy, sustainability and the environment \ıVlthin the context of the UN Sustainable Development GoaJs; awareness of the legal implications of engineering solutions.

      1

      Knowledge of ttıe impacts of engineering practices on society, health and safety, economy, su.stainability and the environment, within the context of the UN Sustainable Development Goals.

      2

      Awareness of the legal implications of engineering solutions

      7

      Ethical Behavlor: Acting in accordance with the principles of the engineering profession. knowledge about ethical ,esponsibility; awareness of being impartial. without discrimination, and being inclusive of diversity.

      1

      Acting in accordance with engineering professional principles. information about ethical responsibility

      2

      Awareness of being impartial and indusive of diversity, without disaiminating on any subject.

      8

      lndividual and Teamwork: Ability to work effectively individually and as a team member or leader on interdis.ciplinary and multidisciplinary teams (face-to-face, remote or hybrid).

      1

      lndividually and within the discipline

      2

      Ability to work effectivefy as a team member or leader in mutti-disciplinary teams (face-to-face, remote or hybrid)

      9

      Verbal and Written Communication: Taking into account the various differences of the target audience (such as education, language, profession) on technical issues.

      1

      Verbal

      2

      Ability to communicate effectively in writing.

      10

      Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.

      1

      Knowledge of business practices such as project management and economic feasibility analysis;

      2

      Awareness of entrepreneurship and innovation.

      11

      Lifelong Learning: Lifelong learning skills that include being able to learn independently and continuously, adapting to new and developing technologies. and thinking questioningly about tedınological changes

      *1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest


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