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Aaditya Ramdas. Byungsoo Jeon. Fabricio Flores. Gi Bum Kim. Jinke Liu. Mauro Moretto. Yimeng Zhang. Ziheng Cai. For example, it includes robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on data mining of historical health records, and speech recognition systems that learn to better understand your speech based on experience listening to you.
This course is designed to give PhD students a thorough grounding in the methods, mathematics and algorithms needed to do research and applications in machine learning. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be at an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate.
If you are interested in this topic, but are not a PhD student, or are a PhD student not specializing in machine learning, you might consider the master's level course on Machine Learning, You can evaluate your ability to take via a self-assessment exam here and see an ML course comparison here. Prerequisites Students entering the class are expected to have a pre-existing working knowledge of probability, linear algebra, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate.
In addition, recitation sessions will be held to revise some basic concepts. Schedule Tentative schedule, might change according to class progress and interest. Every Friday classes is intended to be a recitation to review material or answer homework questions, however this might change if we need a makeup lecture.
Lecture 1: Introduction - What is Machine Learning - slides, notes.Students can post questions and collaborate to edit responses to these questions. Instructors can also answer questions, endorse student answers, and edit or delete any posted content. Piazza is designed to simulate real class discussion.
10701: Machine Learning
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Logistic Regression Slides Annotated slides video. Linear Regression Slides Annotated slides video. Graphical models 1 Annotated slides video. Bishop: Ch 8, through 8. Graphical models 2 slides video. Graphical models 3 annotated slides video. Graphical models 4 annotated slides video. Computational Learning Theory annotated slides video.
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Kernel Methods and SVM's slides video. SVM's II slides video. Active Learning slides video. ML in Computational Biology slides video. Reinforcement Learning I slides video. Reinforcement Learning 2 RL slides Final study guide video. Previous material. Machine learning examples Well defined machine learning problem Decision tree learning. Mitchell: Ch 3 Bishop: Ch Decision Tree learning Review of Probability Annotated slides video. The big picture Overfitting Random variables, probabilities. Mitchell: Naive Bayes and Logistic Regression.You will submit your code for programming questions on the homework to Autolab or Gradescope.
After uploading your code, our grading scripts will autograde your assignment by running your program on a VM.
This provides you with immediate feedback on the performance of your submission. We use Gradescope to collect PDF submissions of open-ended questions on the homework e. For each homework, regrade requests will be open for a maximum of 1 week after the grades have been published.
You receive 6 total grace days for use on any homework assignment except HW1. We will automatically keep a tally of these grace days for you; they will be applied greedily. No assignment will be accepted more than 4 days after the deadline. This has the important implications that you may not use more than 4 graces days on any single assignment.
All homework submissions are electronic see Technologies section below. As such, lateness will be determined by the latest timestamp of any part of your submission. For example, suppose you submit the code part of the homework on time but the written part one hour late, you would have used one of your late days. The email should be sent as soon as you are aware of the conflict and at least 5 days prior to the deadline.
In the case of an emergency, no notice is needed. Official auditing of the course i. Unofficial auditing of the course i. We give priority to students taking the course for a letter grade, so auditors may only take a seat in the classroom is there is one available 10 minutes after the start of class.Overfitting, Random variables and probabilities by Tom Mitchell
Unofficial auditors will not be given access to course materials such as homework assignments and exams. Instructor permission is not required. What grade is the cutoff for Pass will depend on your program. If you have a disability and have an accommodations letter from the Disability Resources office, I encourage you to discuss your accommodations and needs with me as early in the semester as possible.
I will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, I encourage you to contact them at access andrew.
Some of the homework assignments used in this class may have been used in prior versions of this class, or in classes at other institutions, or elsewhere. Solutions to them may be, or may have been, available online, or from other people or sources. It is explicitly forbidden to use any such sources, or to consult people who have solved these problems before. It is explicitly forbidden to search for these problems or their solutions on the internet. You must solve the homework assignments completely on your own.
We will be actively monitoring your compliance. Collaboration with other students who are currently taking the class is allowed, but only under the conditions stated above.Machine learning is concerned with the study and development of automated systems that improve their performance through experience. Examples range from robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on historical health records, and speech recognition systems that lean to better understand your speech based on experience listening to you.
This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning, and related disciplines and applications.
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The Open Learning Initiative has a number materials you may find helpful. See this shared Google spreadsheet for the list of discipline-specific resources. Syllabus Registry The syllabi linked in this Registry have been collected to provide a snapshot of current and past course offerings' syllabi.