Thoughts on CS7641: Machine Learning

I haven’t had time to write the past few months because I was away in Hangzhou to collaborate and integrate with Alibaba. The intense 9-9-6 work schedule (9am – 9pm, 6 days a week) and time-consuming OMSCS Machine Learning class (CS7641) left little personal time to blog.

Thankfully, CS7641 has ended, and the Christmas holidays provide a lull to share my thoughts on it.

Why take this class?

Why take another machine learning course? How will it add to my experience in applying machine learning on real world problems?

Truth be told, I am victim to imposter syndrome. Most of my machine learning knowledge and skills are self-taught, based on excellent MOOCs including those by Andrew Ng and Trevor Hastie and Rob Tibshirani. CS7641 provided an opportunity to re-visit the fundamentals from a different perspective (focusing more on algorithm parameter and effectiveness analysis).

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Impact of the C parameter on SVM’s decision boundary

Additionally, CS7641 covers less familiar aspects of machine learning such as randomised optimisation and reinforcement learning. These two topics were covered at an introductory, survey level, and provided sufficient depth to understand how these algorithms work, and how to apply them effectively and analyse outcomes.

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Effectiveness of randomised optimisation algorithms on the travelling salesman problem (randomised hill climbing, simulated annealing, genetic algorithm, MIMIC)

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