/ Recent content in Home on Class Homepage Hugo -- gohugo.io en-us /docs/grading/categories/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/grading/categories/ Categories # Exceptional Circumstances # Explanation in General section. Assignments (40%) # There will be four assignments. Each one is designed to improve and test your understanding of the materials. Assignments will have both programming and written analysis components. We have 4 big assignments in total. The reason we do not call them projects is because our class has a project as well. Consider each assignment as one individual big project. /docs/resources/collection/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/resources/collection/ GT Resources # For any resources related to Student Engagement and Wellbeing such as Dean of Student’s contacts, Library, etc., please refer to this LINK Class Resources # Helpful Texts # No textbook will be required for this course, however you are strongly encouraged to complete the readings indicated for each class. You may also find the following books very helpful: Learning from data, by Yaser S. Abu-Mostafa Pattern recognition and machine learning, by Christopher Bishop Machine learning, by Tom Mitchell Data Mining: Concepts and Techniques, by Jiawei Han, Micheline Kamber, and Jian Pei The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville Other resources, such as machine learning toolboxes and datasets, will be provided throughout the course. /docs/course-info/course-overview/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/course-info/course-overview/ Course Overview # Important CSE students should note that CS 7641 is not allowed as a substitute for the CSE core course CSE 6740, and that they cannot get credit for both CSE 6740 and CS 7641. This course introduces techniques in machine learning with an emphasis on algorithms and their applications to real-world data. We will investigate the following question: how to computationally extract useful knowledge from data for decision making and task support! /docs/guidelines/general/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/guidelines/general/ General # Attendance # Our class will be offered on campus for both Undergrad (4641) and Grad (7641). Lectures might be recorded IF class has the recording system. Any class that I am able to record [which sometimes does not work even if we have the recording system in place], I will make it available to all students (both undergrad and grad) by the end of the day. The attendance is required for both undergrad and grad. /docs/grading/project-breakdown/proposal/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/grading/project-breakdown/proposal/ Proposal (5%) # The primary goal of the proposal is to identify a problem that can be solved with machine learning. This includes finding and/or creating a dataset as well as developing a plan for the semester. Note: As part of research, it is natural that the project may change from the original proposed. Please be sure to document and justify these changes in the midterm and final report. /docs/course-info/instructors-and-tas/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/course-info/instructors-and-tas/ Instructors and TAs # Instructor # Max Mahdi Roozbahani mahdir@gatech.edu https://mahdi-roozbahani.github.io/ Nimisha Roy nroy9@gatech.edu https://nimisharoy9.wixsite.com/myportfolio Head TAs # Richard Koulen rkoulen3@gatech.edu Ethan Yang eyang301@gatech.edu Ghazal Mirzazadeh gmirzazadeh3@gatech.edu TAs # Lu Li lli652@gatech.edu Huijie Pan hpan77@gatech.edu Deeya Mitra dmitra36@gatech.edu Hannah Glamm hglamm3@gatech.edu Jiaming Zhang jzhang3334@gatech.edu Chandreyi Chakraborty cchakraborty3@gatech.edu Nawal Reza nreza7@gatech.edu Daniel Huang dhuang324@gatech.edu Kevin K Park kpark373@gatech.edu Gryphon Patlin gpatlin3@gatech.edu Emilio Aponte eaponte6@gatech.edu Cac Phan cphan36@gatech.edu Ava Gavin agavin8@gatech.edu Joshua Traynelis jtraynelis3@gatech. /docs/grading/project-breakdown/midterm/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/grading/project-breakdown/midterm/ Midterm Checkpoint (10%) # This is a checkpoint to make sure that you have had major progress in your project. By this point, at least one machine learning model should be implemented and evaluated. Results should be presented using visualizations and quantitative metrics. You will add information to your proposal to create your midterm report. Note: As part of research, it is natural that the project may change from the original proposed. /docs/guidelines/office-hours/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/guidelines/office-hours/ Office Hours # Overview # Office hours will be in a hybrid mode for both online and in-person. We will send an announcement on Ed regarding office hours and when it will start. Please follow the instruction on the Excel Sheet provided on Ed discussion to signup for a slot with one of the TAs. You need to add your name and question of interest. If you require more minutes than the allocated one, please advise the TAs. /docs/course-info/course-schedule-mahdi/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/course-info/course-schedule-mahdi/ Schedule # Important All deadline and due dates in this course will be at 23:59 EST. Scroll horizontally to see the full schedule table on mobile devices Astericks (*) indicate that the lecture slides/notes are available. Week Dates Topics Homework Quizzes Project Readings 1 Jan 12-16 *Course Overview (L1) *Data analysis toolbox - P1 (L2) Q0 - L1,2 (Warmup) GT Honor Code; Debugging Common Errors in NumPy; Heilmeier catechism; Visual Information Theory by Chris Olah; GitHub Pages; YAML Configuration; NumPy Tutorial; Matplotlib Tutorial; seaborn: statistical data visualization; Overleaf for GT students; 2 Jan 19-23 *Linear Algebra (By Dr. /docs/grading/project-breakdown/final/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/grading/project-breakdown/final/ Final Report (15%) # The final report should present and compare at least 3 models that you have developed throughout the semester. Results should be presented using visualizations and quantitative metrics with a heavy focus on comparing the performance and tradeoffs of each approach. You will add information to your midterm to create your final report. Note: As part of research, it is natural that the project may change from the original proposed. /docs/grading/project-breakdown/award_galore/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/grading/project-breakdown/award_galore/ Why Awards Matter # Awards serve as a tangible recognition of the dedication and creativity students bring to their machine learning projects. Each semester, these select honors are chosen from among hundreds of submitted projects, making the competition fierce and underscoring the ingenuity and motivation of participating teams. By highlighting outstanding work, we encourage students to push boundaries, refine technical skills, and communicate findings more effectively. This recognition also benefits students’ academic and professional growth—award recipients can feature their projects on resumes and link directly to showcased work for added visibility. /docs/course-info/course-schedule-nimisha/ Mon, 01 Jan 0001 00:00:00 +0000 /docs/course-info/course-schedule-nimisha/ Schedule # Important All deadline and due dates in this course will be at 23:59 EST. Scroll horizontally to see the full schedule table on mobile devices Week Dates Topics Homework Quizzes Project Readings 1 Jan 12-16 Course Overview (Notes, L1) Data analysis toolbox - P1 (Notes,L2) Q0 - L1,2 (Warmup) GT Honor Code; Debugging Common Errors in NumPy; Heilmeier catechism; Visual Information Theory by Chris Olah; GitHub Pages; YAML Configuration; NumPy Tutorial; Matplotlib Tutorial; seaborn: statistical data visualization; Overleaf for GT students; 2 Jan 19-23 Linear Algebra (Notes, L3) Prob and Stats (Notes, L4) A1 out Jan 23 Q1 - L3 & Syllabus Quiz Correlation vs Covariance; Linear Algebra Review by Zico Kolter; 3 Jan 26-30 Prob and Stats - contd (Notes, L5) Info Theory (Notes, example, L6) Q2 - L4,5 The Differences Between Data, Information and Knowledge Cross Entropy as loss function More about Cross Entropy and KLD; Probability Theory Review by Andrew Moore; 4 Feb 2-6 Info Theory - contd (Notes, L6) Optimization (Notes,L7) Q3 - L6 (CE and KL slides included) Project team composition due Feb 6 KKT for inequality constrained optimization; Why Cross Entropy over MSE for Classification; Gradient Descent short video; Matplotlib Tutorial; NumPy Tutorial; 5 Feb 9-13 Clustering & K-Means (Notes, L8) Data analysis toolbox - P2 (Notes, L9) A1 due Feb-13 A2 out Feb-13 Q4 - L7,8,9 Curse of dimensionality (Euclidean space example); Jupyter Notbook (Kmeans and DBSCAN); 6 Feb 16-20 GMM - Part 1 - WILL BE TAUGHT BY DR.