Leila Wehbe. 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.No more than 3 grace days can be used on any single assignment. NOTE: Any assignment submitted more than 3 days past the deadline will get zero credit. Projects: Each team will be given a total of 3 grace days on the project can be split between project proposals and final reports.
NOTE: Projects submitted more than 3 days past the deadline will get zero credit.
Extensions In general, we do not grant extensions on assignments. There are several exceptions:. For any of the above situations, you may request an extension by emailing the assistant instructor s at bedmunds andrew. 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.
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.The impact of the coronavirus has called for unprecedented steps to protect the health and well-being of the entire community.
Feedback will be provided during a separate Zoom appointment. Signing up for Language Support classes will remain unchanged and can be completed via the Class Registration page. The workshops and seminars themselves will be held remotely via Zoomand a calendar invite will be provided to students who register.
The Calendar listings will also be updated to include Zoom links to allow for drop-in attendance. Appointments for 1-on-1 meetings will be able to be made on the 1-on-1 Consultation page by selecting an appointment slot on the calendar embedded there. These meetings will be held in Zoomand a calendar invite will be provided.Different types of contactors
CMU will transition to remote instruction The impact of the coronavirus has called for unprecedented steps to protect the health and well-being of the entire community. Read the message from President Jahanian. Classes Signing up for Language Support classes will remain unchanged and can be completed via the Class Registration page.Introduction to Machine Learning.
Xing, M. Jordan and R. KarpFeature selection for high-dimensional genomic microarray dataProceedings of the Eighteenth International Conference on Machine Learning. Andrew Y Ng. Kearns and U. Blei et al. Griffiths and M.Overfitting, Random variables and probabilities by Tom Mitchell
Introduction of the course Basic probability Maximum likelihood estimate. Lecture 3 Ziv : Decision trees - Slides updatedVideo. Discriminative classifiers Entropy Information gain Building decision trees. Lecture 5 Ziv : Linear regression - SlidesVideo. Basic model Solving linear regression Error in linear regression Advanced regression models. Lecture 6 Ziv : Logistic regression - SlidesVideo. Logistic regression vs. Bishop, Ch.
Bias-variance decomposition Structural risk minimization Ways to avoid overfitting. Vapnik, V. Combing weak learners Bagging and random forest AdaBoost, algorithem and generalization bounds Gradient boosting. Lecture 12, 13 Ziv : Unsupervised learning - clustering - SlidesVideo.Numpy multivariate normal pdf
Hierarchical clustering K-means and Gaussian mixture models Number of clusters. Lecture 14 Ziv : Semi-supervised learning - SlidesVideo. Re-weighting EM, data augmentation Co-training Detect overfitting.
Realizable vs agnostic PAC learning in finite concept class Sample complexity. Mitchell, Ch. Sample complexity for infinite concept classes VC dimension as a complexity measure Structural risk minimization. An introduction to graphical modelsM.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.
10707 - Deep Learning
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.
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.Whatsapp gb for windows phone
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.The Pornhub team is always updating and adding more porn videos every day.
CMU will transition to remote instruction
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Don't have your phone? Please contact support. Sign in to add this to a playlist. All Professional Homemade.Probability and Estimation Annotated slides video. Naive Bayes Annotated slides video. Logistic Regression Slides Annotated slides video.Via verde ridge hoa
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. Midterm Review PAC learning slides midterm review slides video.
Zoom Video Conferencing
Hidden Markov Models annotated slides. Neural Networks slides video. Learning Representations I slides video.
Learning Representations II slides video. Learning Representations III slides video. 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. Gaussian Bayes classifiers Document classification Brain image classification Form of decision surfaces. Feature selection Overfitting Bias-Variance tradeoff.
Bayes nets representing joint distributions with conditional independence assumptions. D-separation and Conditional Independence Inference Learning from fully observed data Learning from partially observed data. Murphy Graphical Models tutorialM.
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