Lecture notes for "Interdisciplinary Approach to Science, Technology, Engineering and Mathematics - STEM in Education"
Instructor: Dimitris Kastaniotis, PhD
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)
## 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
Projects : Projects for Machine Learning and Data Analysis
Topics covered:
- Clustering
- Dimensionality reduction
- Linear Regression
- Classification
- (Advanced) Collecting data from social media!