COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
CENG 420MACHINE LEARNING & PATTERN RECOGNITION3 + 08th Semester5

COURSE DESCRIPTION
Course Level Bachelor's Degree
Course Type Elective
Course Objective Purpose of this course is to teach fundamental concepts of machine learning and pattern recognition.
Course Content Introduction to machine learning, supervised learning, regression, model order and generalization properties, Bayes decision theory, maximum likelihood method, distance functions, multivariable models and regression, dimensionality reduction and principal component analysis, k-means clustering, decision trees, support vector machines, artificial neural networks and hidden Markov models.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to face

COURSE LEARNING OUTCOMES
1Lists basic pattern recognition concepts
2Explains statistical methods
3Explains linear methods
4Explains nonlinear methods

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12
LO 014422 5    4 
LO 024422 5    4 
LO 034422 5    4 
LO 044422 5    4 
Sub Total161688 20    16 
Contribution442205000040

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)14228
Mid-terms13030
Final examination13030
Total Work Load

ECTS Credit of the Course






130

5

COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2020-2021 Spring1ALPER UĞUR

Course Details
Course Code:  CENG 420 Course Title:  MACHINE LEARNING & PATTERN RECOGNITION
L+P Hour : 3 + 0   Course Code : 1   Language Of Instruction: Turkish Course Semester :  2020-2021 Spring
Course Coordinator :  ASSISTANT PROFESSOR ALPER UĞUR E-Mail:  augur@pau.edu.tr Phone Number : 
Course Location MUH A0253,
Goals : Purpose of this course is to teach fundamental concepts of machine learning and pattern recognition.
Content : Introduction to machine learning, supervised learning, regression, model order and generalization properties, Bayes decision theory, maximum likelihood method, distance functions, multivariable models and regression, dimensionality reduction and principal component analysis, k-means clustering, decision trees, support vector machines, artificial neural networks and hidden Markov models.
Attendance : %70
Topics
WeeksTopics
1 Introduction and Basics
2 Decision Trees, Random forest algorithms
3 Weka app, Regression
4 Classification Rule based naive bayes
5 Classification svm
6 Clustering kmeans
7 Knime software
8 ANN
9 Tensorflow
10 Tensorflow
11 Image Classification
12 Image Classification
13 Project demos
14 Project demos
Materials
Materials are not specified.
Resources
ResourcesResources Language
Weka, https://www.cs.waikato.ac.nz/ml/weka/English
Tensorflow, https://www.tensorflow.org/English
Singh, H. (2019) Practical machine learning and image processing : for facial recognition, object detection, and pattern recognition using python. Berkeley, California: Apress. doi: 10.1007/978-1-4842-4149-3. English
Rebala, G., Ravi, A. and Churiwala, S. (2019), An Introduction to Machine Learning, SpringerEnglish
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam45Final Exam
Midterm Exam25Midterm Exam
Homework20Homework
Term Learning Activity10Term Learning Activity
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes
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