Summary of course
Contents
What? Me worry? No final exam in this course!
What did I learn in school this year?
Topics we have covered this year
Statistical analysis and optimization of data
Machine learning
Learning outcomes and overarching aims of this course
Perspective on Machine Learning
Machine Learning Research
Starting your Machine Learning Project
Choose a Model and Algorithm
Preparing Your Data
Which Activation and Weights to Choose in Neural Networks
Optimization Methods and Hyperparameters
Resampling
Other courses on Data science and Machine Learning at UiO
Additional courses of interest
What's the future like?
Bayesian Machine Learning
Reinforcement Learning
Transfer learning
Adversarial learning
Dual learning
Distributed machine learning
Meta learning
The Challenges Facing Machine Learning
Explainable machine learning
Quantum machine learning
Quantum machine learning algorithms based on linear algebra
Quantum reinforcement learning
Quantum deep learning
Social machine learning
The last words?
Best wishes to you all and thanks so much for your heroic efforts this semester
Preparing Your Data
Shuffle your data
Mean center your data
Why?
Normalize the variance
Why?
Whitening
Decorrelates data
Can be hit or miss
When to do train/test split?
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