CSEP 546 Machine Learning Course Overview and Lecture Topics
Explore the CSEP 546 Machine Learning course taught by Geoff Hulten, featuring topics such as machine learning algorithms, model production, important learning strategies, and real-world applications. Dive into logistic regression, feature engineering, ensemble methods, neural networks, and more. Get insights on organizing machine learning models, connecting ML with users, and approaching your own ML project. Join this informative course to enhance your knowledge in the field of machine learning.
Uploaded on | 0 Views
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
You are allowed to download the files provided on this website for personal or commercial use, subject to the condition that they are used lawfully. All files are the property of their respective owners.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.
E N D
Presentation Transcript
CSEP 546 Machine Learning Lecturer: Geoff Hulten TAs: Kousuke Ariga & Angli Liu
Logistics Course location: https://www.washington.edu/classroom/SAV+260 Course website: https://courses.cs.washington.edu/courses/csep546/18au/ Canvas site: https://canvas.uw.edu/
Introducing Myself Geoff Hulten ghulten@cs.washington.edu https://www.linkedin.com/in/geoff-hulten-58136a1/ What I ve worked on Why I m here
Introducing our TAs Kousuke Ariga koar8470@cs.washington.edu Angli Liu anglil@cs.washington.edu Research interests: Research Interests: My area of research is using external knowledge (knowledge bases, pre-trained embeddings, raw text, etc.) to help improve sequence models, with applications to low-resource language translation, and unsupervised named-entity recognition. Office hours: Mondays 5:30-6:20 Second floor breakout in Allen Center Office Hours: < Let s vote! >
Introducing the Class What types of jobs? Engineering? Data science? Management/PM? Other? Math? Not really? Can do? I think in math? Machine learning experience? This is my first class? Several classes / exploration? Do it for a living? Python? Never used? Some experience? No problem?
Overview of the Course 1) Learn important machine learning algorithms (the tools) 2) Learn how to produce models (use the tools) 3) Learn how to produce working systems (Machine Learning Engineering)
Lecture Overview Week Topics Week Topics Overview of this Course Overview of Machine Learning Evaluating 101: FP/FN and Confusion Matrices Logistic Regression Feature Engineering (Text focus) Evaluating 201: Hold out, error bounds, cross validation ROC Curves and operating points Decision Trees Overfitting and Underfitting Parameters in Modeling Ensembles Machine Learning with an Abuser Deploying Machine Learning models Basics of Computer Vision Clustering and Dimensionality Reduction Instance Based Methods Neural Networks An introduction to mistakes in machine learning 5 1 6 Veterans Day Catch up on topics (maybe a video lecture)... 2 Design Patterns for Machine Learning Reinforcement Learning Other important learning: Bayesian Networks, SVM, HMMs & CRFs Connecting Machine Learning to Users Organizing models in large systems A quick review of the course Approaching your own machine learning project 7 3 8 4 9
Assignments Logistic Regression Feature Engineering (text) Decision Trees Ensembles Clustering Feature Engineering (Vision) Neural Networks Reinforcement Learning Model building & interpreting And more
Evaluation Assignments: Exam: There will be ~8 weeks with assigned work. Each week s work is worth ~10% of the final grade (1 point = 1%). There will be an exam worth 20% of the final grade (although you must score at least 50% on the exam to pass the course). You may hand each assignment in up to two weeks after it is assigned. Except for the last assignment, which is due before the start of the final lecture (so we can submit final course grades in a timely fashion). This will be online and timed, and will be available for 2-3 days. Final week of class? Finals week? Clarity of communication is critical in machine learning, so your answers must be concise and easy to follow. If the TA can t evaluate the answers in reasonable time they will have to give reduced credit. This exam will be based on the assignments, so the best way to prepare will be to do the assignments carefully.
The Textbooks and why All royalties to be donated
