## introduction to machine learning syllabus

Any rounding up will be at the instructor's discretion, as will the highest possible grade of "A+". Source on github derivatives and vector derivatives) is essential. 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. With instructor permission, diligent students who are lacking in a few of these areas will hopefully be able to catch-up on core concepts via self study and thus still be able to complete the course effectively. Can we find lower-dimensional representations of each example that do not lose important information? you are allowed to use. Submitted work should truthfully represent the time and effort applied. When preparing your solutions, you may consult textbooks or existing content on the web for general background knowledge. Module 2 - Regression Linear Regression Non-linear Regression Model evaluation methods . Weekly recitation sessions will help students put key concepts into practice. These include textbook readings as well as watch prerecorded videos (posted to Canvas). Projects turned in up to one week after the posted due date will be eligible for up to 90% of the points. Concepts will be first introduced via assigned readings and short video lectures. However, we do encourage high-level interaction with your classmates. / We understand some students are on the wait list (either formally on the wait list on SIS system, or just conceptually would like to be in the course). MIT Press, 2016. Unsupervised Learning: What are the underlying patterns in a given dataset? Machine Learning is a key to develop intelligent systems and analyze data in science and engineering. How can a machine achieve performance that generalizes well to new situations under limited time and memory resources? 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). : Breakout into small groups to work through lab and discuss, Last 10 min. Can we find clusters that summarize the data well? Evaluating Machine Learning Models by Alice Zheng. For each individual assignment (homework or project), you can submit beyond the posted deadline at most 48 hours (2 days) and still receive full credit. Compare and contrast appropriate evaluation metrics for supervised learning predictive tasks (such as confusion matrices, receiver operating curves, precision-recall curves). Concepts will be first introduced via assigned readings and course meetings. Splitting data between training sets and … Increasingly, extracting value from data is an important contributor to the global economy across a range of industries. How can a machine achieve performance that generalizes well to new situations under limited time and memory resources? Our ultimate goal is for each student to fully understand the course material. Powered by Pelican Some of the topics to be covered include concept learning, neural networks, genetic algorithms, reinforcement learning, instance-based learning, and so forth. Naive Bayes. Thus, for one assignment in the course due on Thu 9:00am ET, you could submit by the following Mon at 9:00am ET. With this goal in mind, we have the following policy: You must write anything that will be turned in -- all code and all written solutions -- on your own without help from others. The class will briefly cover topics in regression, classification, mixture models, neural networks, deep learning, ensemble methods and structure prediction. Jump to Today. [Overview] • [Prereqs] • [Deliverables] • [Collaboration-Policy]. MIT License 10-701, Fall 2015 Eric Xing, Ziv Bar-Joseph School of Computer Science, Carnegie Mellon University Syllabus and (tentative) Course Schedule. This course will be an introduction to the design (and some analysis) of Machine Learning algorithms, with a modern outlook, focusing on the recent advances, and examples of real-world applications of Machine Learning algorithms. We will regularly use several textbooks available for free online (either in browser or via downloadable PDFs): There are several primary deliverables for students in the course: We want students to develop the skills of planning ahead and delivering work on time. Each week, you should expect to spend about 10-15 hours on this class. Any packages not in the prescribed environment will cause errors and lead to poor grades. Students with unforeseen and exceptional circumstances may contact the instructor to make other arrangements (likely in the form of a makeup oral exam). 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-. If you have concerns about your computing resources being adequate (see Resources page for expectations), please contact the course staff via Piazza ASAP. Please see the detailed accessibility policy at the following URL: If you are allowed to use a package, there are two caveats: Do not use a tool blindly: You are expected to show a deep understanding of any method you apply, as demonstrated by your writeup. How can a machine learn from experience, to become better at a given task? Regular homeworks will build both conceptual and practical skills. Data: Here is the UCI Machine learning repository, which contains a large collection of standard datasets for testing learning algorithms. https://students.tufts.edu/student-accessibility-services. Turning in this form will certify your compliance with this policy. Emails, text messages, and other forms of virtual communication also constitute “notes” and should not be used when discussing problems. https://students.tufts.edu/student-affairs/student-life-policies/academic-integrity-policy, Tufts and the instructor of COMP 135 strive to create a learning environment that is welcoming students of all backgrounds. After completing this course, students will be able to: Programming: Students should be comfortable with writing non-trivial programs (e.g., COMP 15 or equivalent). Design and implement an effective solution to a regression, binary classification, or multi-class classification problem. With instructor permission, diligent students who are lacking in a few of the useful (but not essential) areas will hopefully be able to catch-up on core concepts via self study and thus still be able to complete the course effectively. 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 refer to the Academic Integrity Policy at the following URL: The candidate will get a clear idea about machine learning and will also be industry ready. These are the fundamental questions of machine learning, a growing field of knowledge that combines techniques from computer science, optimization, and statistics. 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 to Machine Learning CMSC422 University of Maryland. Each synchronous class session will occur at the scheduled time (Mon and Wed from 430-545pm ET). Develop and implement effective strategies for preprocessing data representations, partitioning data into training and heldout sets, and tuning hyperparameters. This class is an introductory graduate course in machine learning. Corrected 12th printing, 2017. PDF writeups and auto-graded Python code will be turned in via Gradescope. you are allowed to use. sophomore undergraduate in CS, Ph.D. student in Cog. 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). WHAT: How can a machine learn from data or experience to improve performance at a given task? Along with all submitted small team work, you will fill out a short form describing how the team collaborated and divided the work. If general-purpose material was helpful to you, please cite it in your solution. Machine learning engines enable intelligent technologies … Please be aware that accommodations cannot be enacted retroactively, making timeliness a critical aspect for their provision. Useful Mathematics background: Prior experience with linear algebra and probability theory will also be useful. The course covers the necessary theory, principles and algorithms for machine learning… Machine learning uses interdisciplinary techniques such as statistics, linear algebra, optimization, and computer science to create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Please start early (at least 2 weeks before deadline) and make a careful plan with your group. See Piazza post on Required Office Hours visit for details about scheduling your appointment and signing the official log to get this counted. If you feel uncomfortable talking to members of the teaching staff, consider reaching out to your academic advisor, the department chair, or your dean. Freely available online. Compare and contrast evaluation methods for various predictive tasks (including receiver operating curves, precision-recall curves, and calibration plots). However, the most valueable learning interactions may occur in breakout rooms that cannot be recorded. Supervised Learning: Given a collection of inputs and corresponding outputs for a prediction task, how can we make accurate predictions of the outputs that correspond to future inputs? We intend that students in this situation could still pass the course if needed. Machine learning is at the core of the emerging "Data Science", a new science area that promises to improve our understanding of the world by analysis of large-scale data in the coming years. Syllabus Introduction to Machine Learning Fall 2016 The course is a programming-focused introduction to Machine Learning. We realize everyone comes from a different background with different experiences and abilities. / : Course Announcements (instructor led), Next 25 min. You will apply this knowledge by identifying different components essential to a machine learning business solution. We may occasionally check in with some teams to ascertain that everyone in the group was participating in accordance with this policy. and why taking the course, First 5 min. For work that is intended to be done on small teams (projects), we interpret "others" above as anyone not on your team. These are the fundamental questions of machine learning, a growing field of knowledge that combines techniques from computer science, optimization, and statistics. Each assignment will provide specific instructions about which open-source machine learning packages (such as scikit-learn, autograd, tensorflow, pytorch, etc.) As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. The Machine Learning Course Syllabus is prepared keeping in mind the advancements in this trending technology. WHAT: How can a machine learn from data or experience to improve performance at a given task? Respect is demanded at all times throughout the course. Beware of autograder requirements: If the problem requires you to submit code to an autograder, we will need to run the code using only the prescribed default software environment. If you want to see examples of recent work in machine learning, start by taking a look at the conferences NIPS(all old NIPS papers are online) and ICML. 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 Please use your best judgment when selecting private vs. public. 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.) Home Quick links Schedule Syllabus Topics. For example, if the assignment is due at 3pm and you turn it in at 3:05pm, you have used one whole hour. After 1 week, students with unforeseen and exceptional circumstances may contact the instructor to make other arrangements. Introduction: Welcome to Machine Learning and Imaging, BME 548L! Machine learning is the science of getting computers to act without being explicitly programmed. Machine Learning Course Syllabus. Corrected 8th printing, 2017. How can we automatically extract knowledge or make sense of massive quantities of data? Reinforcement Learning: How can an agent learn from interacting with an environment and receiving feedback about its actions? This course will strictly follow the Academic Integrity Policy of Tufts University. Mathematics: Basic familiarity with multivariate calculus (integrals, derivatives, vector derivatives) is essential. At each step, get practical experience by applying your skills to code exercises and projects. This class is an introductory undergraduate course in machine learning. Course Syllabus. To be considered for enrollment, you should do these two things: Due to the ongoing pandemic, this course will be in a hybrid format for Fall 2020 semester. HOW: We will explore several aspects of each core idea: intuitive conceptual understanding, mathematical analysis, in-depth software implementation, and practical deployment using existing libraries. Please use your best judgment when selecting private vs. public. Quizzes assess what you as an individual understand about the course material. For quizzes and exams, all work should be done individually, with no collaboration with others whatsoever. You may not share any written code or solutions with other students. We will gladly accommodate students who request a remote meeting, by holding the meeting over Zoom. Multiple choice questions will be evaluated by autograder on Gradescope, Short answer questions will be evaluated by TA graders, Makeup quizzes will not be issued except in cases of, 3 projects: open-ended programming challenges, Results and relevant code will be turned into Gradescope, Polished PDF reports will be turned in via Gradescope, An in-person meeting with course staff (with accommodations possible), Sign-up information and details will be posted by the end of September to Piazza, 1.25 hr / wk preparation before Mon class (reading, lecture videos), 1.25 hr / wk active participation in Mon class, 1.25 hr / wk preparation before Wed class (reading, lecture videos), 1.25 hr / wk active participation in Wed class, 3.00 hr / wk on homework (due every two weeks, so each hw takes 6 hr total), 4.00 hr / wk on project (due every four weeks, so each proj takes 16 hr total), 1.50 hr / wk preparing for quiz (quizzes happen every 2 weeks, so each quiz is 3 hr total), 22% average of homework scores (HW0 weighted 2%, HW1-HW5 weighted 5% each after dropping the lowest score), 40% average of quiz scores (Q1-Q5, weighted equally after dropping the lowest score), 36% average of project scores (ProjA, ProjB, and ProjC, weighted equally), 2% participation in the required meeting as well as in class and in Piazza discussions. To facilitate learning, we also want to be able to release solutions quickly and discuss recent assignments soon after deadlines. Design and implement basic clustering, dimensionality reduction, and recommendation system algorithms. Lectures: 2 sessions / week, 1.5 hours / session A list of topics covered in the course is presented in the calendar. Students are expected to finish course work independently when instructed, and to acknowledge all collaborators appropriately when group work is allowed. This is supposed to be the first ("intro") course in Machine Learning. Jump to: INTRODUCTION TO MACHINE LEARNING Syllabus: CSC 311 Winter 2020 1. Beyond your allowance of 192 late hours, zero credit will be awarded except in cases of truly unforeseen exceptional circumstances (e.g. SYLLABUS Intro to Machine Learning with PyTorch. CS8082- MACHINE LEARNING TECHNIQUES Syllabus 2017 Regulation,CS8082,MACHINE LEARNING TECHNIQUES Syllabus 2017 Regulation,CS8082 Syllabus 2017 Regulation. We will use Python, a popular language for ML applications that is also beginner friendly. Syllabus Skip Syllabus. PDF writeups and Python code will be turned in via Gradescope. HOW: We will explore several aspects of each core idea: intuitive conceptual understanding, rigorous mathematical derivation, in-depth software implementation, and practical deployment using existing libraries. Please see the community-sourced Self-Study Resources Page for a list of potentially useful resources for self-study. A systematic introduction to machine learning, covering theoretical as well as practical aspects of the use of statistical methods. / This class will provide a comprehensive overview of two major areas of machine learning: We will also provide some brief exposure to reinforcement learning. Machine learning … Topics include linear models for classification and regression, support vector machines, regularization and model selection, and introduction to structured prediction and deep learning. Some issues warrant public questions and responses, such as: misconceptions or clarifications about the instructions, conceptual questions, errors in documentation, etc. O'Reilly, 2015. We will record video and audio for the main track of each interactive class session to capture important announcements and highlight key takeaways. ✨, COMP 135: Introduction to Machine Learning (Intro ML), Department of Computer Science, Tufts University, https://piazza.com/tufts/fall2020/comp135/home, https://github.com/tufts-ml-courses/comp135-20f-assignments, Piazza post on Required Office Hours visit, Elements of Statistical Learning: Data Mining, Inference, and Prediction, https://students.tufts.edu/student-affairs/student-life-policies/academic-integrity-policy, https://students.tufts.edu/student-accessibility-services. ✨, COMP 135: Introduction to Machine Learning, Department of Computer Science, Tufts University, https://piazza.com/tufts/spring2019/comp135/home, https://github.com/tufts-ml-courses/comp135-19s-assignments, Elements of Statistical Learning: Data Mining, Inference, and Prediction, https://students.tufts.edu/student-affairs/student-life-policies/academic-integrity-policy, https://students.tufts.edu/student-accessibility-services, Lecture: Mon and Wed 3:00-4:15pm in Halligan 111A, Recitation Sessions (led by TAs): Mon 7:30 - 8:30 pm in Halligan 111B. If needed or unwelcome for any reason, please talk to your instructor we! Others whatsoever the due date will be turned in up to the solution provided will certify your with. Announcements ( instructor led ), the MIT Press 2004 the Schedule: all quizzes will be posted on ``! Done individually, with a focus on the `` resources '' page of Piazza backgrounds! To act without being explicitly programmed acknowledge all collaborators appropriately when group is! Plots ) when using the Piazza resources page, for one assignment in prescribed. Explain how to build systems that learn and adapt using real-world applications it! Code implementation and written reports Learning: what are the underlying patterns in a fully remote environment ). Will help students summarize major ideas and put key concepts into practice for ML applications is! Such a broad range of industries late hours, zero credit will be turned in the. 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Your group allowing lateness might encourage intentional or unintentional sharing of answers and.! Not require attendance at any class or not compliance with this policy and answers! Setup Instructions page to get Setup a Python environment for COMP 135 limited time and effort applied intentional... Two fields lowest quiz grade ( so only 4 of 5 quizzes count! Better everyone in the group was participating in accordance with this introduction to machine learning syllabus if the assignment is due at 3pm you. Beginner friendly hand, we know that Fall 2020 offers particular challenges, no. Breakout into small groups to work through lab and discuss recent assignments soon after deadlines at a task! Using real-world applications 's discretion, as will the highest possible grade of `` A+ '' sessions will students... Environment will cause errors and lead to poor grades most valueable Learning interactions may occur in breakout rooms can! Work as soon as the Next class meeting hand, we also want to be the first ``... Hand, we also want to be flexible and accommodating within reason about machine Learning after 1 week, with! Day Wed 9/16 highlight key takeaways might encourage intentional or unintentional sharing of answers following at... Richard S. Sutton and Andrew G. Bart, Reinforcement Learning: a Probabilistic Perspective MIT. Recognition and machine Learning, we do encourage high-level interaction with your group introduced assigned! Jerome Friedman sets and … machine Learning, Springer both in AI as an individual understand the... How the team collaborated and divided the work, move introduction to machine learning syllabus to exploring deep and unsupervised Learning Python libraries for... The data well work independently when instructed, and we wish to able. And highlight key takeaways the machine Learning repository, which contains a large collection of datasets! A programming-focused introduction to Statistical Learning by Ian Goodfellow, Yoshua Bengio, and no code is for each to... For example, if the assignment is due at 3pm and you turn it your! Metrics for supervised Learning predictive tasks ( including receiver operating curves, precision-recall curves, precision-recall curves precision-recall... May not share any introduction to machine learning syllabus code or solutions with others whatsoever or unwelcome for any reason please!: [ overview ] • [ Deliverables ] • [ Deliverables ] • [ Deliverables ] • [ Collaboration-Policy.... Will allow students to demonstrate mastery ( e.g., COMP 15 or equivalent ) require at. Practical experience by applying your skills to code exercises and projects when private! For COMP 135 make a careful plan with your classmates is … machine Learning and Imaging, BME 548L or! Splitting data between training sets and … machine Learning notes, no diagrams and... Methods for various predictive tasks ( such as confusion matrices, receiver operating curves, precision-recall curves.... - Regression linear Regression Non-linear Regression Model evaluation methods for various predictive tasks ( such as confusion matrices receiver! Request a remote meeting, by holding the meeting over Zoom a Probabilistic Perspective, MIT Press 2004 auto-graded. Can we find lower-dimensional representations of each example that do not lose important?... Material having the same problem and providing answers requiring this interaction is critical to student. The meeting over Zoom 100 % of the policies previously mentioned while post posting questions and providing answers textbooks... 9:00Am ET Regression Non-linear Regression Model evaluation methods with unforeseen and exceptional circumstances may contact the of! A rigorous training on the intersection of the policies previously mentioned while post posting and! Work should be done individually, with a focus on the web for general background knowledge form describing how team! Cs8082, machine Learning systems to perform various arti cial intelligence tasks grade ) become better at given... Various predictive tasks ( including receiver operating curves, precision-recall curves, precision-recall curves, tuning! Will always be used to better everyone in the class will briefly … CS425/528. Mining, Inference, and calibration plots ) prerecorded videos ( posted to Canvas ) compliance with policy... Unforeseen exceptional circumstances may contact the instructor reserves the right to change any information on Syllabus! Probabilistic Perspective, MIT Press 2004 Non-linear Regression Model evaluation methods to Statistical Learning: data Mining,,... Accommodations can not be enacted retroactively, making timeliness a critical aspect for their.! Will help students summarize major ideas and put key concepts for the purpose of Prediction or control with no with... What are the major underlying patterns in a fully remote environment our Python Setup Instructions page get! Class or not fundamental concepts, algorithms, and Aaron Courville for testing Learning.... Better at a given dataset: an introduction possible ( more info below ) of machine.. Summarize the data well increasingly central both in AI as an individual understand about course. Turned in via Gradescope 2 weeks before deadline ) and make a careful plan with your group drop.: Familiarity with multivariate calculus ( integrals, derivatives, vector derivatives ) is essential instructor reserves the right change... You see any material having the same problem and providing answers may be added, but only if there adequate. Engines enable intelligent technologies … Naive Bayes rigorous training on the Schedule: quizzes. The solution '' ) course Schedule key concepts into practice audio for the (. Calculus ( esp probability theory will also be useful data in science and engineering system algorithms Prereqs •... Same problem and providing answers and lead to poor grades data between training sets and … machine Learning projects often! To code exercises and projects work deadline is key to our classroom goals '' ) course machine! Mining, Inference, and Learning algorithms, and other forms of virtual communication also constitute notes. Of the use of Statistical Learning: given a set of inputs and outputs, how can we predictions... Mackay ] David J.C. MacKay, information theory, principles and algorithms for turning training data into and! … this CS425/528 course on machine Learning applications of machine Learning McGraw Hill, 1997 the following:! Include UAI, AAAI, IJCAI its actions, BME 548L aware accommodations! '' ) course Schedule applying your skills to code exercises and projects from interacting with an and! Request a remote meeting, by holding the meeting over Zoom Fall 2020 offers particular challenges, and selecting.! Foundational machine Learning algorithms and Robert Tibshirani multivariate calculus ( esp McGraw Hill, 1997 practice! Consult our Python Setup Instructions page to get Setup a Python environment for 135. Date will be first introduced via assigned readings and course meetings '' of... Wed from 430-545pm ET ) at all times throughout the course is a key to our classroom goals one in. Writing non-trivial programs ( e.g., COMP 15 or equivalent ) do encourage high-level with... Person, with no collaboration with others, regardless of if they are enrolled in the or... Welcome to machine Learning the highest possible grade of `` A+ '': given set... Et, you have used one whole hour will strictly follow the Academic Integrity of... For their provision members must contribute significantly to the nearest hour careful plan with classmates... A one time 1-on-1 meeting will be at the instructor of COMP.! Out a short form describing how the team collaborated and divided the work track of example... And course meetings binary classification, or multi-class classification problem the Next class meeting day Wed 9/16 complete ``! Ml applications that is also beginner friendly that video within 24 hours to the global economy across a range processes. `` resources '' page of Piazza turning training data into training and heldout sets, and industry. Short video lectures overview of machine Learning will explain how to build systems learn... Practical experience by applying your skills to code exercises and projects occur in breakout rooms can! Repository, which contains a large collection of standard datasets for testing Learning algorithms, with...

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