The course was taught by Professors Charles Isbell and Micheal Littman. Both are really awesome. Contrary to most other courses on the topic, they have managed to make the course content easy to understand and interesting, without losing out on any of its essences. All videos are structured as conversations between the Profs where one acts as the teacher and other as the student – very effective.
The course was a literature survey and general introduction into the various areas in ML. It was primarily divided into 3 modules:
- Supervised learning – where we are given a dataset with labels (emails classified as spam or not). You try to predict the labels for future data based on what you’ve already seen or ‘learned’.
- Techniques include Decision Trees, K-Nearest Neighbours, Support Vector Machines (SVM), Neural Networks etc
- Unsupervised learning – all about finding patterns in unlabeled data. Eg: Group similar products together (clustering) based on customer interactions. This can be really helpful in recommendations etc.
- Randomized Optimization, clustering, feature selection and transformation etc.
- Reinforcement learning – the most exciting one (IMHO). This overlays many concepts we usually consider as part of Artificial Intelligence. RL is about incentivizing machines to learn various tasks (such as playing chess) by providing different rewards.
- Markov Decision Processes, Game Theory etc.
- I found the concepts in GT such as the Prisoners Dilemma, Nash Equilibrium etc. and how they tie into RL interesting.
All of these are very vast subjects in themselves. The assignments were designed in such a way that we got to work with all of these techniques at least to some extent. The languages and libraries that we use were left to our choice, though guidance and recommendations were provided. Through that, got the opportunity to work with Weka, scikit-learn and BURLAP.
Overall, enjoyed the course really well. Hoping to take courses like Reinforcement Learning (link) to learn more about the topics in upcoming semesters.