Artificial Neural Network

Mode of Delivery

Face to Face

Prerequisites and Co-Requisites

None

Recommended Optional Programme Components

Not Required

Course Contents

Introduction. In artificial neural network (ANN) learning process. ANN applications. The first artificial neural networks. Feedforward (feed forward) ANN. Feedback (recurrent) neural networks. Radial-based (radial basis) function networks. Associative (associative) memory networks. Principal component analysis. The self-organizing (self-organizing maps) ANN. Supporting be learned vector quantization (learning vector quantization) networks. Adaptive resonance theory (Adaptive Resonance Theory) networks. Modular neural networks. Neurodinamik programming.

Weekly Schedule

1) Basic Concepts
2) Supervised Machine Learning and Methods
3) Supervised Machine Learning and Methods
4) Supervised Machine Learning and Methods
5) Supervised Machine Learning and Methods
6) Deep Learning and Methods
7) Deep Learning and Methods
8) Midterm Examination
9) Deep Learning and Methods
10) Reinforcement Learning and Methods
11) Reinforcement Learning and Methods
12) Community Learning
13) Community Learning
14) Project Presentations
15) Project Presentations
16) Final Examination

Recommended or Required Reading

1- Andriy Burkov, Machine Learning Engineering
2- Andreas C. Müller, Sarah Guido, Introduction to Machine Learning with Python: A Guide for Data Scientists
3- Kevin P. Murphy, Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

Planned Learning Activities and Teaching Methods

1) Lecture
2) Question-Answer
3) Discussion
4) Drill and Practice
5) Group Study
6) Self Study
7) Project Based Learning

Assessment Methods and Criteria

Contribution of Midterm Examination to Course Grade	40%
Contribution of Final Examination to Course Grade	60%
Total	100%

Language of Instruction

Turkish

Work Placement(s)

Not Required