This course provides an introduction on machine learning. Questions may be posted as either private (viewable only by yourself and course staff) or public (additionally viewable by all students for the course registered on Piazza). Machine learning (ML) is a set of techniques that allow computers to learn from data and experience, rather than requiring humans to specify the desired behaviour manually. We will regularly use several textbooks available for free online (either in browser or via downloadable PDFs): There are three primary tasks for students throughout the course: Late work policy for homeworks and projects: We want students to develop the skills of planning ahead and delivering work on time. Introduction to Machine Learning Inductive Classification Decision-Tree Learning Ensembles Experimental Evaluation Computational Learning Theory Rule Learning and Inductive Logic Programming We will post relevant links to virtual class meetings (and office hours) on the "Resources" page of Piazza. Identify relevant real-world problems as instances of canonical machine learning problems (e.g. If you feel uncomfortable talking to members of the teaching staff, consider reaching out to your academic advisor, the department chair, or your dean. If you have concerns about your computing resources being adequate (see Resources page for expectations), please contact the course staff via Piazza ASAP. Machine learning is the science of getting computers to act without being explicitly programmed. This is supposed to be the first ("intro") course in Machine Learning. To facilitate learning, we also want to be able to release solutions quickly and discuss recent assignments soon after deadlines. For extreme personal issues only: Mike Pietras • Rui Chen • Manh (Duc) Nguyen • Minh Nguyen • Yirong (Wayne) Tang. / ... the instructor reserves the right to change any information on this syllabus or in other course materials. Along with all submitted small team work, you will fill out a short form describing how the team collaborated and divided the work. For homeworks and projects and papers, we have the following policy for student work: You must write anything that will be turned in -- all code and all written solutions -- on your own without help from others. Describe basic dimensionality reduction and recommendation system algorithms. / Machine Learning is a key to develop intelligent systems and analyze data in science and engineering. These are the fundamental questions of machine learning. Students are expected to finish course work independently when instructed, and to acknowledge all collaborators appropriately when group work is allowed. Tufts and the instructor of COMP 135 strive to create a learning environment that is welcoming students of all backgrounds and abilities. For example, if the assignment is due at 3pm and you turn it in at 3:05pm, you have used one whole hour. Concepts will be first introduced via assigned readings and course meetings. These are the fundamental questions of machine learning, a growing field of knowledge that combines techniques from computer science, optimization, and statistics. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Unsupervised Learning: What are the underlying patterns in a given dataset? Identify relevant ethical and social considerations when deploying a supervised learning or representation learning method into society, including fairness to different individuals or subgroups. Please see the detailed accessibility policy at the following URL: Please see the detailed accessibility policy at the following URL: Finally, open-ended practical projects -- often organized like a contest -- will allow students to demonstrate mastery. With these goals in mind, we have the following policy: Each student will have 120 total late hours (5 late days) to use throughout the semester across the 8 homeworks and 3 projects. This meeting will happen by default in person (but only in a setting where it is safe to do so). PDF writeups will be turned in via Gradescope. You may work out solutions together on whiteboards, laptops, or other media, but you are not allowed to take away any written or electronic information from joint work sessions with others. In this course, you will learn about principles and algorithms for turning training data into effective automated predictions. Topics include linear models for classification and regression, support vector machines, regularization and model selection, and introduction to structured prediction and deep learning. Emails, text messages, and other forms of virtual communication also constitute “notes” and should not be used preparing solutions. Please refer to the Academic Integrity Policy at the following URL: Some of the topics to be covered include concept learning, neural networks, genetic algorithms, reinforcement learning, instance-based learning, and so forth. Final grades will be computed based on a numerical score via the following weighted average: When assigning grades, the following scale numerical scale will be used: Each assignment will provide specific instructions about which open-source machine learning packages (such as scikit-learn, tensorflow, pytorch, shogun, etc.) This class is an overview of machine learning and imaging science, with a focus on the intersection of the two fields. Course Syllabus. MIT Press, 2015. Date Lecture Topics Readings and useful links Anouncements; Module 1: Supversived Learning: Thu 9/3: When using the Piazza forum, you should be aware of the policies previously mentioned while post posting questions and providing answers. This class will provide a comprehensive overview of two major areas of machine learning: We will also provide some brief exposure to reinforcement learning. You may not share any code or solutions with others, regardless of if they are enrolled in the class or not. You may work out solutions together on whiteboards, laptops, or other media, but you are not allowed to take away any written or electronic information from joint work sessions. [Overview] • [Class-Format] • [Wait-List] • [Prereqs] • [Deliverables] • [Late-Work] • [Collaboration-Policy]. Design and implement basic clustering, dimensionality reduction, and recommendation system algorithms. Machine Learning Course Syllabus. derivatives and vector derivatives) is essential. This course will strictly follow the Academic Integrity Policy of Tufts University. When preparing your solutions, you may always consult textbooks, materials on the course website, or existing content on the web for general background knowledge. Introduction: Welcome to Machine Learning and Imaging, BME 548L! Syllabus Skip Syllabus. Evaluating Machine Learning Models by Alice Zheng. Machine learning is an exciting and fast-moving field of computer science with many recent consumer applications (e.g., Microsoft Kinect, Google Translate, Iphone's Siri, digital camera face detection, Netflix recommendations, Google news) and applications within the sciences and medicine However, you cannot ask for answers through any question answering websites such as (but not limited to) Quora, StackOverflow, etc. No notes, no diagrams, and no code. We do count a small part of a student's grade as participation, which can be fulfilled either via being active in Piazza forum discussions or in live class discussions. Some issues are better with private posts, including: debugging questions that include extensive amounts of code, questions that reveal a portion of your solution, etc. We are currently at capacity, but some students may drop the course and leave openings for others (usually we see 10-20 openings in the first week of classes as schedules shift). For extreme personal issues only: Rui Chen • Sheng Xu • Victor Arsenescu • Xi Chen • Xiaohui Chen • Lily Zhang • Zhitong Zhang. However, you cannot ask for answers through any question answering websites such as (but not limited to) Quora, StackOverflow, etc. Regular homeworks will build both conceptual and practical skills. Some other related conferences include UAI, AAAI, IJCAI. We do not require attendance at any class or track attendance. We will record video and audio for the main track of each interactive class session to capture important announcements and highlight key takeaways. See Piazza post on Required Office Hours visit for details about scheduling your appointment and signing the official log to get this counted. By the first homework, students will be expected to do the following without much help: Midterm will be during a normally scheduled class period, Final will be at the appointed final exam hour and location for this class, Makeup exams will not be issued except in cases of, 8 homework assignments (written and code exercises). : Course Announcements (instructor led), Next 25 min. O'Reilly, 2015. Please use your best judgment when selecting private vs. public. 2nd Edition, Springer, 2009. After the due date, you can receive zero credit. This course provides a broad introduction to modern machine learning. For each individual assignment, you can submit beyond the posted deadline at most 96 hours (4 days) and still receive full credit. Concepts will be first introduced via assigned readings and short video lectures. Can we find lower-dimensional representations of each example that do not lose important information? Here's our recommended break-down of how you'll spend time each week: Final grades will be computed based on a numerical score via the following weighted average: When assigning grades, the following scale numerical scale will be used: This means you must earn at least an 0.83 (not 0.825 or 0.8295 or 0.8299) to earn a B instead of a B-. We may occasionally check in with some teams to ascertain that everyone in the group was participating in accordance with this policy. Submitted work should truthfully represent the time and effort applied. 10-701, Fall 2015 Eric Xing, Ziv Bar-Joseph School of Computer Science, Carnegie Mellon University Syllabus and (tentative) Course Schedule. https://students.tufts.edu/student-accessibility-services, MIT License WHAT: How can a machine learn from data or experience to improve performance at a given task? We have found that requiring this interaction is critical to improving student engagement and retention. INTRODUCTION TO MACHINE LEARNING Syllabus: CSC 311 Winter 2020 1. Lectures: 2 sessions / week, 1.5 hours / session A list of topics covered in the course is presented in the calendar. For example, if the assignment is due at 3pm and you turn it in at 3:30pm, you have used one whole hour. We intend that students in this situation could still pass the course if needed. Self-Study Resources Page for a list of potentially useful resources for self-study. Introduction to Machine Learning Course. As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. CS8082- MACHINE LEARNING TECHNIQUES Syllabus 2017 Regulation,CS8082,MACHINE LEARNING TECHNIQUES Syllabus 2017 Regulation,CS8082 Syllabus 2017 Regulation. Corrected 8th printing, 2017. How can we automatically extract knowledge or make sense of massive quantities of data? Module 1 - Introduction to Machine Learning Applications of Machine Learning Supervised vs Unsupervised Learning Python libraries suitable for Machine Learning . Textbook Tom Mitchell, Machine Learning McGraw Hill, 1997. We will gladly accommodate students who request a remote meeting, by holding the meeting over Zoom. Source on github Machine learning … Prof. Alexander Ihler. Each student is responsible for shaping this environment: please participate actively and respectfully! Programming: Students should be comfortable with writing non-trivial programs (e.g., COMP 15 or equivalent). However, the most valueable learning interactions may occur in breakout rooms that cannot be recorded. Students with exceptional circumstances should contact the instructor to make other arrangements. Learning, like intelligence, covers such a broad range of processes that it is dif- cult to de ne precisely. Instructional material (readings, notes, and videos) will always be "prerecorded" and released on the Schedule page in advance, under "Do Before Class". When preparing your solutions, you may consult textbooks or existing content on the web for general background knowledge. / WHY: Our goal is to prepare you to effectively apply machine learning methods to problems that might arise in "the real world" -- in industry, medicine, education, and beyond. Turning in this form will certify your compliance with this policy. 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