Introduction to Machine Learning: Opportunities and Applications
Delve into the world of machine learning with exciting research opportunities at UH-DAIS in Summer 2024. Explore the two options available, including a special problems course and a SURF Summer Scholarship. Understand the essence of machine learning, its applications, and the subfields it encompasses. Discover the significance of optimizing performance through example data and past experiences. Uncover the role of statistics and computer science in enhancing algorithms for model learning in unknown realms.
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News February 26, 2024 Summer 2024 UH-DAIS Research Opportunities (need to contact Raunak or me by March 1, if you are interested; links are in the 4368 Webpage). 2 options: Take special problems course in Summer 2024 SURF Summer Scholarship ($4000, cannot count as course credit, competitive) Group Project Groups have been set up; will announce the groups later. Midterm Exam on We., March 6, 2:30p (more about it in the Feb. 28 lecture) Today s Class A Gentle Introduction to Machine Learning Reinforcement Learning Group Project Task and Goals 1
Christoph F. Eick: A Gentle Introduction to Machine Learning
Why Learn? Machine learning is programming computers to optimize a performance criterion using example data or past experience. There is no need to learn to calculate payroll Learning is used when: Human expertise does not exist (navigating on Mars), Humans are unable to explain their expertise (speech recognition) Solution changes in time (routing on a computer network) Solution needs to be adapted to particular cases (case- based reasoning) 3
What is Machine Learning? Machine Learning Study of algorithms that improve their performance at some task with experience Optimize a performance criterion using example data or past experience. Role of Statistics: Inference from a sample Role of Computer science: Efficient algorithms to Solve the optimization problems to learn models Learn models for unknown and changing worlds Representing and evaluating the model for inference 4
Machine Learning: Classification Models Classification Algorithms Training Data Classifier (Model) NAME RANK Mike Mary Bill Jim Dave Anne YEARS TENURED 3 7 2 7 6 3 Assistant Prof Assistant Prof Professor Associate Prof Assistant Prof Associate Prof no yes yes yes no no IF rank = professor OR years > 6 THEN tenured = yes 5
Subfields of Machine Learning Supervised Learning Classification Prediction Unsupervised Learning Association Analysis Clustering Preprocessing and Summarization of Data Reinforcement Learning Transfer Learning Deep Learning Density Estimation Activities Related to Models Learning parameters of models Choosing/Comparing/Evaluating models 6
Growth of Machine Learning Machine learning is preferred approach to Speech recognition, Natural language processing Computer vision Medical outcomes analysis Robot control Computational biology This trend is accelerating Improved machine learning algorithms Improved data capture, networking, faster computers Software too complex to write by hand New sensors / IO devices Demand for self-customization to user, environment It turns out to be difficult to extract knowledge from human experts failure of expert systems in the 1980 s. 7 Alpydin & Ch. Eick: ML Topic1
Resources: ML Conferences International Conference on Machine Learning (ICML) European Conference on Machine Learning (ECML) Neural Information Processing Systems (NIPS) International Joint Conference on Artificial Intelligence (IJCAI) and AAAI (Top US AI Conference) ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), IEEE Int. Conf. on Data Mining (ICDM) 8
