Course Descripton
IST 597.
Principles of Machine Learning. Statistical Learning Theory and Applications (decision theoretic foundations, probabilistic generative models, discriminative models, kernel machines, representative algorithms); Algorithmic Learning Theory and Applications (mistake bound, PAC, occam, curriculum learning models, representative algorithms); Probabilistic graphical models (Bayesian networks, Markov models, Hidden Markov Models, Latent Variable models, Probabilistic relational models, Stochastic process models, grammars, representative algorithms); Selected topics (multiinstance learning, multilabel learning, multirelational learning, structured label learning, distributional learning, deep learning, longitudinal learning); Learning predictive models from Big Data (learning from large data, high dimensional data, distributed data, linked data, platforms and tools).
Course Staff
Course Schedule
Lectures: Monday, Wednesday 2:30pm  3:45pm, E206 Westgate Building
Office Hours:
Vasant Honavar: Monday, Wednesday 4:00pm  5:00pm, E335 Westgate Building.
Sam Gur: Tuesday, Thursday: 4:00pm5pm
Course Prerequisites
The prerequisites for the course include knowledge of programming, discrete mathematics (set theory, grapth theory, logic), calculus, basic probability theory and statistics, and data structures (lists, trees, graphs etc.) and algorithms (design and analysis).
In addition, students are expected to have the writing and presentation skills necessary for preparing written reports and presentations based on term projects.
If you are not sure whether you have the necessary background, please talk to the instructor.
Target Audience
This course is targeted to graduate and advanced undergraduate students in Information Sciences and Technology, Computer Science, Engineeering, Life Sciences, Health Sciences, Environmental Sciences, Physical Sciences, Social Sciences, among other disciplines at Pennsylvania State University who are interested in developing and applying machine learning algorithms to extract knowledge from data. The course should also be accessible to, and potentially of interest to graduate students from a variety of disciplines and programs including Computer Science and Engineering, Bioengineering, , Operations Research, Bioinformatics and Genomics, Neuroscience, Electrical Engineering, Cognitive Psychology, Statistics.
Course Objectives
The course aims to provide an introduction to the principles, techniques, and applications of Machine Learning. It covers rigorous statistical and algorithmic foundations of machine learning and a suite of representative algorithms for learning predictive models from data. Lab assignments are used to help clarify basic concepts. Upon successful completion of the course, students will have a broad understanding of machine learning algorithms and their use in extracting knowledge from data. Students will have designed and implemented several machine learning algorithms. Students will also be able to identify, forumulate and solve machine learning problems that arise in practical applications. Students will have a knowledge of the strengths and weaknesses of different machine learning algorithms (relative to the characteristics of the application domain) and be able to adapt or combine some of the key elements of existing machine learning algorithms to design new algorithms as needed. The students will have an understanding of the current state of the art in machine learning and be able to begin to conduct original research in machine learning.
