STEM_MachineLearningClass

Interdisciplinary Approach to Science, Technology, Engineering and Mathematics - STEM in Education- Lectures site

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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| ppt 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!