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Cyprus International University
TR

STATISTICAL MACHINE LEARNING

Course
DASC311 - STATISTICAL MACHINE LEARNING
Department
Data Science - English - Undergraduate
Course Type
Course
Status
Required
Language
English
Credit
3
ECTS
6
T+P+L
3 + 0 + 0
Course Coordinator(s)
Asst. Prof. Dr. Yasemin BAY
Prerequisite
-
Keywords

Course Description

In this course, statistical machine learning which has roots in computer science, artificial intelligence and statistics is covered and a broad understanding of algorithms that allow computers to improve their performance through the process of ‘learning’ and enable them to make decisions and predictions is provided. Fundamental methods are taught and applied to real data. The term statistical in title emphasizes the statistical techniques, which form dominant approaches to machine learning. The course integrates methodology with theoretical underpinnings, computational elements, and statistical theory issues. By completion of this course, students are expected to learn about supervised and unsupervised learning approaches to speech recognition, internet search, bioinformatics, image and audio signal analysis, data mining and exploratory data analysis.

STATISTICAL MACHINE LEARNING

Evaluation Tools (Active Term)

Item Type Weight (%)
Midterm Midterm 30
Final Final 40
Project Project 20
Assignment Assignment 10
Total 100

Course outcomes

No course outcomes have been defined yet.

Course Syllabus

No weekly content has been defined yet.

Reference Books & Course Materials

No reference books have been listed.

Learning Outcomes

No learning outcomes have been defined.

Program Outcomes

  1. P01 Be able to apply knowledge of programming
  2. P02 Be able to design software systems of varying complexity
  3. P03 Be able to identify, categorize, and develop solutions for computer orientated challenges.
  4. P04 Be able to demonstrate autonomy and responsibility in managing computer programming projects
  5. P05 Be able to follow the state of the arts concepts in computer technology
  6. P06 Be able to design, implement, and evaluate a computational system to meet desired needs within realistic constraints.
  7. P07 Be able to use appropriate techniques, skills, and tools necessary for computing practice.
  8. P08 Be able to apply appropriate technologies and techniques for the collection and analysis of organizational and environmental data to facilitate evidence-based decision making
  9. P09 Be able to apply effective communication skills consistent with the professional environment -
  10. P10 Be able to apply effective collaboration skills in teamwork consistent with the professional environment -

Po-Lo Matrix

The PO-LO matrix has not been populated yet.