Algorithms and principles involved in machine learning; focus on perception problems arising in computer vision, natural language processing and robotics; fundamentals of representing uncertainty, learning from data, supervised learning, ensemble methods, unsupervised learning, structured models, learning theory and reinforcement learning; design and analysis of machine perception systems; design and implementation of a technical project applied to real-world datasets (images, text, robotics).
We are witnessing an explosion in data – from billions of images shared online to Petabytes of tweets, medical records and GPS tracks, generated by companies, users and scientific communities. Applications of machine learning and perception are increasing rapidly as more techniques are developed and implemented to address a wide range of scientific and societal problems. Many universities are expanding programs in machine learning and perception, and employers are increasingly recognizing the importance of such knowledge. Students trained in a deeper understanding of machine learning techniques will be better equipped to make fundamental contributions to research in machine learning, and applied areas such as perception (vision, text, speech), robotics, bioinformatics, etc.
Percentage of Course
|1. Overview of Machine Learning & Perception a) Learning from data b) Overfitting, regularization, cross-validation||5%|
|2. Supervised Learning a) Nearest Neighbor b) Naive Bayes c) Logistic Regression d) Support Vector Machines e) Neural Networks f) Decision Trees||25%|
|3. Unsupervised & Semi-Supervised Learning a) Clustering (K-means, GMMs) b) Factor Analysis (PCA, LDA)||10%|
|4. Learning Theory a) Bias and Variance b) Probably Approximately Correct (PAC) Learning||10%|
|5. Structured Models a) Bayesian Networks b) Hidden Markov Models||10%|
|6. Reinforcement Learning||10%|
|7. Applications of ML to Perception a) Vision b) Natural Language Processing||15%|
|8. Design and implementation of a technical project||15%|