Study Guide

Objective

After completing the course, the student:can apply modern design methods and tools in embedded software design.has up-to-date knowledge on current topics in embedded software

Content

The course covers current topics in embedded software. See implementation plan for details.

Materials

1) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition by Aurélien Géron, OREILLY, 2020

2) Hands-on Scikit-Learn for Machine Learning Applications, by David paper, Apress publications, 2020

3) Guide to intelligent data analysis: how to intelligently make sense of real data. by Berthold, Michael R., et al. Springer Science & Business Media, 2010.

Teaching methods

The course includes theoretical lectures, mandatory practical lab works, self-study/ home assignments, and a final project work.

NOTE: Due to the current pandemic, remote implementation of the lab works is still one possibility, so be prepared for last notice changes.

Exam schedules

Exam dates will be announced (it would be most likely the next session after the last lecture)

International connections

Lectures (onsite and online lectures will be arranged)
Self-study materials from online sources
Project work (will be discussed during the course)
Exam (the date will most likely the next session after the last lecture in December.)

Student workload

lab works 12x3h = 36h
lectures 14x2h = 28h
exam =4h
exam preparation 32h
TOTAL 100h

Content scheduling

Chapter 1: The Machine Learning Landscape
Chapter 2: End-to-End Machine Learning Project
Chapter 3: Classification
Chapter 4: Training Models
Chapter 5: Support Vector Machines
Chapter 6: Decision Trees
Chapter 7: Ensemble Learning and Random Forests
Chapter 8: Dimensionality Reduction
Chapter 9: Unsupervised Learning Techniques
Chapter 10: Introduction to Artificial Neural Networks with Keras
Chapter 11: Training Deep Neural Networks
Chapter 12: Custom Models and Training with TensorFlow
Chapter 13: Loading and Preprocessing Data with TensorFlow
Chapter 14: Deep Computer Vision Using Convolutional Neural Networks
Chapter 15: Processing Sequences Using RNNs and CNNs

Evaluation scale

H-5

Assessment methods and criteria

Must pass Final Exam:
60% -> 1
68% -> 2
76% -> 3
84% -> 4
92% -> 5
Mandatory lab works: individual Lab performance matters
Mandatory lectures
Mandatory project work (failure in delivery= failed)