Lecture notes for "Interdisciplinary Approach to Science, Technology, Engineering and Mathematics - STEM in Education"

Instructor: Dimitris Kastaniotis, PhD

Lecture 1:pdf| ppt Introduction to Machine Learning- Supervised and Unsupervised Learning methods

Topics covered:

• Machine Learning definition
• Unsupervised Learning (Dimensionality Reduction, Blind Source Separation, Clustering)
• Supervised Learning (Linear models for classification and Regression, Kernel Methods, Lazy Learning)
• Brief introduction to Optimization
• Model Selection Here sound_files.zip you can find the sound files used to demonstrate Independent Component Analysis (slides 59- 67)

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## Lecture 2:[pdf](https://www.dropbox.com/s/oi3m0ntpxfg8qi2/Lecture2_share.pdf?dl=0)| [ppt](https://www.dropbox.com/s/oi3m0ntpxfg8qi2/Lecture2_share.pdf?dl=0) Introduction to Deep Learning and Natural Language Processing Topics covered:
- Perceptron and Multilayer Linear Networks - Convolutional Neural Networks - Recurrent Neural Networks - Generative models - Natural Language Processing

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Projects : Projects for Machine Learning and Data Analysis

Topics covered:

• Clustering
• Dimensionality reduction
• Linear Regression
• Classification
• (Advanced) Collecting data from social media!