Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1BBM409Machine Learning Laboratory0+2+01406.09.2024

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program COMPUTER ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The subject matter of this course is to make the students practice the fundamentals of machine learning.
Course Content Overview of Machine Learning
Linear Regression, Least Squares
Machine Learning Methodology
Probability and Linear Algebra Basics
Statistical Estimation: MLE, MAP, Naive Bayes Classifier
Linear Classification Models: Logistic Regression, Linear Discriminant Functions, Perceptron
Support Vector Machines
Decision Tree Learning
Ensemble Methods: Bagging, Boosting
Clustering
Neural Networks
Principle Component Analysis
Course Methods and Techniques Lecture, Problem Solving, Drill and Practice, Preparing and/or Presenting Reports
Prerequisites and co-requisities ( BBM102 ) and ( BBM104 ) and ( BBM406 )
Course Coordinator None
Name of Lecturers Associate Prof.Dr. Ahmet Burak Can
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Mitchell T., Machine Learning, McGraw Hill, 1997. Alpaydın E., Introduction to Machine Learning, The MIT Press, 2004. Artificial Intelligence: A Modern Approach (3rd Edition), Stuart Russell and Peter Norvig. Prentice Hall, 2009. Bayesian Reasoning and Machine Learning, David Barber, Cambridge University Press, 2012. Introduction to Machine Learning (3rd Edition), Ethem Alpaydin, MIT Press , 2014. Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press, 2012. Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2006.
Course Notes Mitchell T., Machine Learning, McGraw Hill, 1997.
Alpaydın E., Introduction to Machine Learning, The MIT Press, 2004.
Artificial Intelligence: A Modern Approach (3rd Edition), Stuart Russell and Peter Norvig. Prentice Hall, 2009. ?
Bayesian Reasoning and Machine Learning, David Barber, Cambridge University Press, 2012. ?
Introduction to Machine Learning (3rd Edition), Ethem Alpaydin, MIT Press , 2014. ?
Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press, 2012.
Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2006.


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Midterm Exam 6 % 20
Practice 3 % 80
Total
9
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 2 28
Hours for off-the-c.r.stud 8 3 24
Practice 3 18 54
Preparation for Midterm Exam 6 2 12
Total Work Load   Number of ECTS Credits 3,93333333333333 118

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 After completing the course, the students will practice the fundamentals of machine learning through several applications
2 The students will gain the ability of performing scientific analysis within the machine learning problems.
3  
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Overview of Machine Learning, Nearest Neighbor Classifier
2 Linear Regression, Least Squares
3 Machine Learning Methodology, Probability and Linear Algebra Basics
4 Statistical Estimation: MLE, MAP, Naive Bayes Classifier
5 Linear Classification Models: Logistic Regression, Linear Discriminant Functions, Perceptron
6 Neural Networks
7 Deep Neural Networks
8 Midterm exam
9 Support Vector Machines
10 Kernels, Kernel Trick for SVMs, Decision Tree Learning
11 Ensemble Methods: Bagging, Boosting
12 Clustering
13 Dimensionality Reduction
14 Course wrap-up, Project Presentations
15
16

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5 2 4 4 4 4 4 3 3 2
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
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