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DASC488

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: Supervised learning

unsupervised learning

reinforcement learning

back propagation algorithmi

convolutional neural network

recurrent neural network

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.

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

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

Learning Outcomes

No learning outcomes have been defined.

Program Outcomes

P01 Be able to apply knowledge of programming
P02 Be able to design software systems of varying complexity
P03 Be able to identify, categorize, and develop solutions for computer orientated challenges.
P04 Be able to demonstrate autonomy and responsibility in managing computer programming projects
P05 Be able to follow the state of the arts concepts in computer technology
P06 Be able to design, implement, and evaluate a computational system to meet desired needs within realistic constraints.
P07 Be able to use appropriate techniques, skills, and tools necessary for computing practice.
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
P09 Be able to apply effective communication skills consistent with the professional environment -
P10 Be able to apply effective collaboration skills in teamwork consistent with the professional environment -

Po-Lo Matrix

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