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Cyprus International University
TR

NEURAL NETWORKS AND DEEP LEARNING

Course
DASC488 - NEURAL NETWORKS AND DEEP LEARNING
Department
Data Science - English - Undergraduate
Course Type
Course
Status
Required
Language
English
Credit
3
ECTS
6
T+P+L
3 + 0 + 1
Course Coordinator(s)
Prof. Dr. Tolgay KARANFİLLER
Prerequisite
-
Keywords

Course Description

In this course Artificial Neural Network (ANN), which is a mathematical paradigm imitating biological neural network for reasoning and problem solving is covered. With focus on practice, the course cover ANN models for various real-world applications and offers a hands-on introduction to deep learning tools and techniques. Students do not need to have an extensive math background to understand this course. The course covers McCulloch-Pitts model and basic neural network models, multilayer perceptron, associative memory, self-organizing feature maps, recurrent neural networks, etc. and reviews applications of these models to various types of data. Upon completion of this course, students will gain a broad understanding of the context of neural networks and deep learning, the data demands of deep learning and the parameters for neural networks.

NEURAL NETWORKS AND DEEP LEARNING

Evaluation Tools (Active Term)

Item Type Weight (%)
Kısa Sınav 1 Quiz 15
Kısa Sınav 2 Quiz 15
Vize Midterm 30
Final Final 40
Total 100

Course outcomes

No course outcomes have been defined yet.

Course Syllabus

Week Topic
Week 1 Introduction to Artificial Neural Networks
Week 2 Basic Neuron Models — The McCulloch–Pitts Model
Week 3 The Perceptron and Its Learning Rule
Week 4 ADALINE / Delta Rule and Its Limitations
Week 5 The Multilayer Perceptron (MLP)
Week 6 Training the MLP and First Hands-On Model
Week 7 Deep Learning Tools and Techniques
Week 8 Midterm Exam Week
Week 9 Midterm Exam Week
Week 10 Convolutional Neural Networks (CNN)
Week 11 Recurrent Neural Networks (RNN)
Week 12 Advanced Sequence Modeling with LSTM / GRU
Week 13 Associative Memory — Hopfield Networks
Week 14 Self-Organizing Feature Maps (SOM / Kohonen)
Week 15 Week 13: Applications, Data Requirements, and Course Review

Reference Books & Course Materials

  1. 01 Neural Networks and Deep Learning: A Textbook (2. Baskı, 2023) Charu C. Aggarwal · Springer
  2. 02 Deep Learning with Python (2. Baskı, 2021) François Chollet (Keras'ın yaratıcısı) · Manning
  3. 03 Grokking Deep Learning (2019,) Andrew Trask · Manning

Learning Outcomes

No learning outcomes have been defined.

Program Outcomes

  1. P01 Demonstrate comprehensive knowledge of key concepts across the breadth of effective application and use of MIS and innovative information technologies in organizations.
  2. P02 Demonstrate autonomy and responsibility in managing MIS projects and improving organizational processes
  3. P03 Demonstrate comprehensive understanding of appropriate enterprise frameworks, theories from the MIS to research and assess contemporary issues in the field and related allied fields and disciplines
  4. P04 Apply MIS knowledge to facilitate the acquisition, development, deployment, and management of information systems
  5. P05 Apply MIS knowledge to the exploitation of opportunities created by information technology innovations ensuring the alignment between MIS strategy and organizational strategy
  6. P06 Demonstrate ethical reasoning in relation to crucial MIS issues such as privacy, information security, and ethical use of information
  7. P07 Apply appropriate technologies and techniques to the collection and analysis of organizational and environmental data to facilitate evidence-based decision-making
  8. P08 Analyse organizational data to accurately identify organizational problems and propose solutions using MIS
  9. P09 Apply effective communication skills consistent with the professional environment
  10. P10 Apply effective collaboration skills consistent with the professional environment

Po-Lo Matrix

The PO-LO matrix has not been populated yet.