Introduction to Machine Learning

Introduction to machine learning; concept learning: General-to-specific ordering of hypotheses, Version spaces Algorithm, Candidate elimination algorithm; Supervised Learning: decision trees, Naive Bayes, Artificial Neural Networks, Support Vector Machines, Overfitting, noisy data, and pruning, Measuring Classifier Accuracy; Linear and Logistic regression; Unsupervised Learning: Hierarchical Agglomerative Clustering. k-means partitional clustering; Self-Organizing Maps (SOM) k-Nearest-neighbor algorithm; Semi-supervised learning with EM using labeled and unlabeled data; Reinforcement Learning: Hidden Markov models, Monte Carlo some inference Exploration vs. Exploitation trade-offs, Markov Decision Processes, Ensemble Learning, Using committees of multiple hypotheses, Bagging and Boosting.

Hours: XYZ where X = Lecture, Y = Lab, Z = Credit
All hours are per week.
3 Lab hours constitute 1 credit hour
1 credit hour implies 1 lecture of 50mins per academic week. 16 weeks in total.
Pre-Requisite courses are courses required to be completed before this course may be taken
Co-Requisite courses are courses required to be taken along with this course