Details - Tematika

Advanced Programming Techniques (GAINBAN-HALAPROG-1)

Basic data

Name and type of the study programme

Computer Science Engineering, undergraduate program

Curriculum

2022

Classes / consultation hours

2 + 0 + 2 (L+S+Labs)

Credits

4 credits

Theory – Practice

Theory: 50%, Practice: 50%

Recommended semester

Semester 5

Study mode

full-time

Prerequisites

Programming Paradigms and Techniques

Evaluation type

Mid-term evaluation

Course category

Compulsory

Language

English

Instructors

Responsible instructor

Dr. Drenyovszki Rajmund

Responsible department

Department of Information Technologies

Instructor(s)

Dr. Drenyovszki Rajmund, Prof. Dr. Johanyák Zsolt Csaba

Checked by

Kovács Márk

Course objectives

Students will learn about the Python language and its applications in machine learning and data processing.

Course content

Lectures

1. Applications of Python. Python data structures. List comprehension, dict comprehension. Mathematical calculations and data structures (NumPy), visualization (Matplotlib). Object oriented programming in Python. Exception handling. Python Standard Library. 2. Machine learning basics. 3. Linear regression, gradient descent. 4. Normal equation, Polynomial regression 5. Binary classification, Datasets, training, validation and test sets, K-Fold validation, Confusion Matrix 6. Classification metrics (TN, FP, FN, TP), F1 Score, precision, recall, ROC 7. Logistic regression, decision tree, structured data 8. Rosenblatt perceptron, linear separability, automatic differentiation, Backpropagation algorithm 9. Neural network basics 10. Convolutional Neural Networks, image processing algorithms: classification, detection, segmentation 11. Transfer learning 12. Optimization, Linear Programming 13. Midterm test

Labs

1. Applications of Python. Variables, basic data structures, 2. Python advanced data structures: list, tuples, set and dictionary. 3. List comprehension, dict comprehension. 4. Mathematical calculations and data structures (NumPy). Visualization (Matplotlib). 5. Object oriented programming in Python. Exception handling. Python Standard Library. 6. Linear regression in Python, Implementation of the Gradient descent algorithm. 7. MNIST binary classification, metrics. 8. Logistic regression, decision tree, structured data 9. Rosenblatt perceptron, linear separability, automatic differentiation, Backpropagation algorithm 10. Neural network basics 11. Convolutional Neural Networks, image processing algorithms: classification, detection, segmentation. Transfer learning 12. Optimization, Linear Programming 13. Misterm test

Acquired competences

Knowledge

- He/she knows the main programming paradigms, programming languages, development tools. His/her knowledge covers the modelling of IT systems, creation of database based systems, as well as the structure, operation and implementation of computer networks. His/her knowledge covers the characteristics of intelligent systems, the specificity of mobile application development, the management of state-of-the art general purpose operating systems, as well as the aspects of IT security. - He/she is familiar with the important software development methodologies, and the notation systems for IT designs and documentation. - He knows the vocabulary and special terms of the engineering profession in the Hungarian and English languages at least on the basic level.

Skills

- He/she is able to develop applications, program client-server and WEB, mobile operating systems, develop multiplatform systems. He/she can apply his/her knowledge acquired during his/her study to acquire deeper knowledge in the field of information engineering and to process special literature and solve problems related to information technology. - He/she is able to fulfill analytical, specification, planning, development and operation tasks, in addition, he/she applies the development methodology, debugging, testing and quality assurance methods in his/her field. - He/she cooperates with other computer science engineers, electrical engineers during team work, and with other experts during the analysis and solution of a problems. - He/she constantly improves his/her knowledge and keeps up with the development of the computer engineering profession.

Attitude

- He/she makes an effort to work efficiently and to high standards.

Autonomy and responsibilities

–

Requirements, evaluation and grading

Mid-term study requirements

Attending classes, reviewing and supplementing what you have heard at home based on the recommended literature. Completion of assigned homework. The practical grade is obtained by obtaining 20 out of 40 points in the theoretical exam and 30 out of 60 points in the project assignment. The theoretical exam may be made up once. During the semester, the lecturer will announce additional opportunities to gain extra points in the lectures.

Generative AI usage

Use of GAI tools is permitted in a limited manner (e.g., for literature search support or specific tools). In this case, the course instructor is responsible for defining where and how GAI tools may be used in assignments. The course description must specify in detail how GAI tools may be used during the course.

Study aids, laboratory background

Advanced Programming Techniques (Moodle)

Readings

Compulsory readings

Google's Python Class: https://developers.google.com/edu/python Kaggle Learn: https://www.kaggle.com/learn Andriy Burkov: The hundred-page machine learning book, http://themlbook.com/ Andriy Burkov: The Hundred-Page Language Models Book: hands-on with PyTorch, True Positive Inc. (January 18, 2025), ISBN-13:978-1778042737, On-line: https://www.thelmbook.com/