Mojtaba Jafaritadi
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.
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.
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)
English
02.09.2020 - 05.03.2021
02.07.2020 - 02.09.2020
0 - 42
Engineering and Business
The lectures cover the main theories, techniques, and algorithms in machine learning and basics of deep learning, starting with fundamental concepts such as regression/classification and ending up with more advanced topics such as artificial neural networks and model selection and performance estimation. The main goal is to extend context and background knowledge around AI, machine learning, and deep learning.
The course in general gives an overview of many machine learning and deep learning methods that can be used to build models and systems based on observed data. After the course, students should understand the main principles of machine learning and pattern recognition methods and steps needed for applying them in real applications. The student especially learns the core concepts of classification, gradient descent, model evaluation, overfitting, and underfitting and is able to find a suitable balance between these extremes in a given problem at hand.
Kupittaa Campus
H-5
Exam dates will be announced (it would be most likely the next session after the last lecture)
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.)
lab works 12x3h = 36h
lectures 14x2h = 28h
exam =4h
exam preparation 32h
TOTAL 100h
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