Y. Emre Yildiz

Homework - Machine Learning

Week 1
1. Giving Computers the Ability to Learn from Data
2. Training Machine Learning Algorithms for Classification
Week 2
1. A Tour of Machine Learning Classifiers Using Scikit-learn
2. Building Good Training Sets – Data Preprocessing
Week 3
1. Compressing Data via Dimensionality Reduction
2. Learning Best Practices for Model Eva. and Hyperparameter Tuning
Week 4
1. Combining Different Models for Ensemble Learning
2. Applying Machine Learning to Sentiment Analysis
Week 5
1. Embedding a Machine Learning Model into a Web Application
2. Predicting Continuous Target Variables with Regression Analysis
Week 6
1. Working with Unlabeled Data – Clustering Analysis
2. Training Artificial Neural Networks for Image Recognition
Week 7
1. Parallelizing Neural Network Training with Theano
2. Thinking in Machine Learning
3. Midterm (Sample Questions)
Week 8
1. Tools and Techniques
2. Turning Data into Information
Week 9
1. Models – Learning from Information
2. Linear Models
Week 10
1. Neural Networks
2. Features – How Algorithms See the World
Week 11
1. Learning with Ensembles
2. Design Strategies and Case Studies
Week 12
1. Unsupervised Machine Learning
2. Deep Belief Networks
Week 13
1. Stacked Denoising Autoencoders
2. Convolutional Neural Networks
Week 14
1. Semi-Supervised Learning
2. Text Feature Engineering
3. Ensemble Methods
4. Additional Python Machine Learning Tools

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