INTERNATIONAL BURCH UNIVERSITY
Faculty of Engineering and Natural Sciences
Department of Information Technologies
2016-2017

SYLLABUS
Code Name Level Year Semester
CEN 359 Introduction to Machine Learning Undergraduate 3 Spring
Status Number of ECTS Credits Class Hours Per Week Total Hours Per Semester Language
Area Elective 5 2 + 2 125 English

Instructor Assistant Coordinator
Zeynep Orhan, Assist. Prof. Dr. Zeynep Orhan Zeynep Orhan, Assist. Prof. Dr.
[email protected] [email protected] no email

In this course, you will get hands-on experience with machine learning from a series of practical case-studies.

COURSE OBJECTIVE
At the end of the first course you will have studied
- how to predict house prices based on house-level features,
- analyze sentiment from user reviews, retrieve documents of interest,
- recommend products, and search for images.
- Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.

COURSE CONTENT
Week
Topic
  1. Introduction to Machine Learning
  2. Regression and Classification
  3. Clustering and Recommender Systems
  4. WEKA: Introduction, Input
  5. WEKA: Output, Algorithms
  6. WEKA: Evaluation, Implementation
  7. WEKA: Implementation
  8. Midterm exams
  9. WEKA Practical Sessions: GUI Components
  10. WEKA Practical Sessions: Evaluation and Simple Classifiers
  11. Python Practical Sessions: Scikit/NumPy/Pandas/MatplotLib
  12. Python Practical Sessions: Scikit/NumPy/Pandas/MatplotLib
  13. Project implementations
  14. Project Presentations and improvements
  15. Project Presentations and improvements

LABORATORY/PRACTICE PLAN
Week
Topic
  1. Introduction to Machine Learning
  2. Regression and Classification
  3. Clustering and Recommender Systems
  4. WEKA: Introduction, Input
  5. WEKA: Output, Algorithms
  6. WEKA: Evaluation, Implementation
  7. WEKA: Implementation

  1. Midterm exams
  2. WEKA Practical Sessions: GUI Components
  3. WEKA Practical Sessions: Evaluation and Simple Classifiers
  4. Python Practical Sessions: Scikit/NumPy/Pandas/MatplotLib
  5. Python Practical Sessions: Scikit/NumPy/Pandas/MatplotLib
  6. Project implementations
  7. Project Presentations and improvements
  8. Project Presentations and improvements

TEACHING/ASSESSMENT
Description
  • Interactive Lectures
  • Practical Sessions
  • Excersises
  • Presentation
  • Assignments
Description (%)
Method Quantity Percentage (%)
Quiz315
Homework420
Midterm Exam(s)115
Final Exam130
+Project120
Total: 100
Learning outcomes
  • Identify potential applications of machine learning in practice
  • Describe the core differences in analyses enabled by regression, classification, and clustering
  • Select the appropriate machine learning task for a potential application
  • Apply regression, classification, clustering, retrieval, recommender systems and deep learning
  • Represent your data as features to serve as input to machine learning models
  • Assess the model quality in terms of relevant error metrics for each task
  • Utilize a dataset to fit a model to analyze new data
  • Using WEKA for these problems
TEXTBOOK(S)
  • Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) 4th Edition by Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal

ECTS (Allocated based on student) WORKLOAD
Activities Quantity Duration (Hour) Total Work Load
Lecture (14 weeks x Lecture hours per week)14228
Laboratory / Practice (14 weeks x Laboratory/Practice hours per week)14228
Midterm Examination (1 week)122
Final Examination(1 week)122
Preparation for Midterm Examination11010
Preparation for Final Examination11616
Assignment / Homework/ Project4624
Seminar / Presentation11515
Total Workload: 125
ECTS Credit (Total workload/25): 5