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Below is the List of Distinguished Final Year 100+ Machine Learning Projects Ideas or suggestions for Final Year students you can complete any of them or expand them into longer projects if you enjoy them.Nonlinear Reconstruction of Genetic Networks Implicated in AML.Aaron Goebel, Mihir Mongia .[pdf]Can Machines Learn Genres.Aaron Kravitz, Eliza Lupone, Ryan Diaz.[pdf]Identifying Gender From Facial Features.Abhimanyu Bannerjee, Asha Chigurupati.[pdf]Equation to LaTeX.Abhinav Rastogi, Sevy Harris.[pdf]Intensity prediction using DYFI.Abhineet Gupta.[pdf]Artificial Intelligence on the Final Frontier – Using Machine Learning to Find New Earths.Abraham Botros.[pdf]Life Expectancy Post Thoracic Surgery.Adam Abdulhamid, Ivaylo Bahtchevanov, Peng Jia.[pdf]Making Sense of the Mayhem- Machine Learning and March Madness.Adam Ginzberg, Alex Tran.[pdf]Better Reading Levels through Machine Learning.AdamGall.[pdf]What are People Saying about Net Neutrality.Adison Wongkar, Christoph Wertz.[pdf]Bird Species Identification from an Image.Aditya Bhandari, Ameya Joshi, Rohit Patki.[pdf]Stay Alert.Aditya Sarkar, Quentin Perrot, Julien Kawawa.[pdf]A bigram extension to word vector representation.Adrian Sanborn, Jacek Skryzalin.[pdf]Mining for Confusion – Classifying Affect in MOOC Learners’ Discussion Forum Posts.Akshay Agrawal, Shane Leonard.[pdf]Cardiac Arrhythmias Patients.AlGharbi Fatema, Fazel Azar, Haider Batool.[pdf]Prediction of Average and Perceived Polarity in Online Journalism.Albert Chu, Kensen Shi, Catherine Wong.[pdf]Cardiac Dysrhythmia Detection with GPU-Accelerated Neural Networks.Albert Haque.[pdf]Nicolas Sanchez Ruck Those Stats!.Alejandro Sanchez.[pdf]Classifying Wikipedia People Into Occupations.Aleksandar Gabrovski.[pdf]Classification of Soil Contamination.Aleo Mok.[pdf]Automated Essay Grading.Alex Adamson, Andrew Lamb, Ralph Ma.[pdf]Relative and absolute equity return prediction using supervised learning.Alex Alifimoff, Axel Sly.[pdf]Seizure Prediction from Intracranial EEG Recordings.Alex Fu, Spencer Gibbs, Yuqi Liu.[pdf]Predicting Seizure Onset with Intracranial Electroencephalogram(EEG) Data.Alex Greaves, Arushi Raghuvanshi, Kai-Yuan Neo.[pdf]Classifying Complex Legal Documents.Alex Ratner.[pdf]Machine Learning Applied to the Detection of Retinal Blood Vessels.Alex Yee.[pdf]Survival Outcome Prediction for Cancer Patients.Alexander Herrmann .[pdf]Predicting Cellular Link Failures to Improve User Experience on Smartphones.Alexander Tom, Srini Vasudevan.[pdf]Yelp Personalized Reviews.Alexis Weill, Thomas Palomares, Arnaud Guille.[pdf]KMeansSL.Alfred Xue, Colin Wei.[pdf]Strength in numbers_ Modelling the impact of businesses on each other.Amir Sadeghian, Hakan Inan, Andres Noetzli.[pdf]Correlation Based Multi-Label Classification.Amit Garg, Jonathan Noyola, Romil Verma.[pdf]Landmark Recognition Using Machine Learning.Andrew Crudge, Will Thomas, Kaiyuan Zhu.[pdf]CarveML an application of machine learning to file fragment classification.Andrew Duffy.[pdf]rClassifier.Andrew Giel,Jon NeCamp,HussainKader.[pdf]Using Vector Representations to Augment Sentiment Analysis Training Data.Andrew McLeod, Lucas Peeters.[pdf]What Project Should I Choose.Andrew Poon.[pdf]Analyzing Vocal Patterns to Determine Emotion.Andy Sun, Maisy Wieman.[pdf]Predicting the Commercial Success of Songs Based on Lyrics and Other Metrics.Angela Xue, Nick Dupoux.[pdf]Application Of Machine Learning To Aircraft Conceptual Design.Anil Variyar.[pdf]Extracting Word Relationships from Unstructured Data.Anirudha Bhat, Krithika Iyer, Rahul Venkatraj.[pdf]Machine Learning for Predicting Delayed Onset Trauma Following Ischemic Stroke.Anthony Ma, Gus Liu.[pdf]Classifying Online User Behavior Using Contextual Data.Anunay Kulshrestha, Akshay Rampuria, Aditya Ramakrishnan.[pdf]Real Time Flight Path Optimization Under Constraints Using Surrogate Flutter Function.Arthur Paul-Dubois-Taine.[pdf]Real-Time Dense Map Matching with Naive Hidden Markov Models Delay versus Accuracy.Arun Jambulapati, Juhana Kangaspunta, Youssef Ahres, Loek Janssen.[pdf]Prediction Function from Sequence in Venom Peptide Families.Arvind Kannan, G. Seshadri.[pdf]Restaurant Recommendation System.Ashish Gandhe.[pdf]Home Electricity Load Forecasting.Atinuke Ademola Idowu, Pawel Kupsc, Sonya Mollinger.[pdf]Learning Dota 2 Team Compositions.Atish Agarwala, Michael Pearce.[pdf]Applying Deep Learning to derive insights about non-coding regions of the genome.Avanti Shrikumar, Anna Saplitski, Sofia Luna Frank-Fischer.[pdf]Classification of Higgs Jets as Decay Products of a Randall-Sundrum Graviton at the ATLAS Experiment.Aviv Cukierman, Zihao Jiang.[pdf]SemenFertilityPrediction.Axel Guyon,Florence Koskas,Yoann Buratti.[pdf]Sentiment Analysis Using Semi-Supervised Recursive Autoencoders and Support Vector Machines.Bahareh Ghiyasian, Yun Fei Guo.[pdf]Classifying Syllables in Imagined Speech using EEG Data.Barak Oshri, Nishith Khandwala, Manu Chopra.[pdf]Abraham Starosta-Typeguess.Baris Akis, Mariano Sorgente.[pdf]Predicting Usefulness of Yelp Reviews.Ben Isaacs, Xavier Mignot, Maxwell Siegelman.[pdf]Predicting Soccer Results in the English Premier League.Ben Ulmer, Matt Fernandez.[pdf]Detecting Heart Abnormality using ECG with CART.Ben Zhou, Gaspar Garcia, Paurakh Rajbhandary.[pdf]Down and Dirty with Data.Bharat Arora, Roger Davidson, Christopher Wildman.[pdf]Hierarchical Classification of Amazon Products.Bin Wang, Shaoming Feng.[pdf]Predicting high-risk countries for political instability and conflict.Blair Huffman, Emma Marriott, April Yu.[pdf]Machine Learning Implementation in live-cell tracking.Bo Gu.[pdf]Any Given Sunday.Bobak Moallemi, Matthew Wilson, Steven Hoerning.[pdf]P300 Error Detection.Boyeaux Felix,Chatoor Nehan.[pdf]Automated Canvas Analysis for Painting Conservation.Brendan Tobin.[pdf]Office Appliance Classification.Brock Petersen, Gerrit de Moor, Elissa Goldner.[pdf]Sentiment Analysis on Movie Reviews.Cai Xiao, Ya Wang.[pdf]Predicting Mobile Application Success.Cameron Tuckerman.[pdf]Modeling Activity Recognition Using Physiological Data Collected from Wearable Technology.Cezanne Camacho, Jennifer Li, Jeffrey Yang.[pdf]Neural Network Joint Language Model.Charles Qi.[pdf]Yelp Recommendation System Using Advanced Collaborative Filtering.Chee Hoon Ha.[pdf]Prediction of Yelp Review Star Rating using Sentiment Analysis.Chen Li, Jin Zhang.[pdf]Classification of Bad Accounts in Credit Card Industry.Chengwei Yuan.[pdf]Classification Of Musical Playing Styles.Chet Gnegy.[pdf]Email Filtering By Response Required.Chris Knight.[pdf]Forecasting Utilization in City Bike-Share Program.Christina Lee, David Wang, Adeline Wong.[pdf]Recommender.Christopher Aberger.[pdf]Predicting Cell Type-Specific Chromatin States from Genetic Regulatory Networks.Christopher Probert, Anthony Ho.[pdf]Pose Estimation Based on 3D Models.Chuiwen Ma, Liang Shi.[pdf]Visual Localization and POMDP for Autonomous Indoor Navigation.Chulhee Yun, Sungjoon Choi.[pdf]Contours and Kernels-The Art of Sketching.Dan Guo,Paula Kusumaputri,Amani Peddada.[pdf]Indoor Positioning System Using Wifi Fingerprint.Dan Li, Le Wang, Shiqi Wu.[pdf]Predicting air pollution level in a specific city.Dan Wei.[pdf]Prediction of Transcription Factors that Regulate Common Binding Motifs.Dana Wyman, Emily Alsentzer.[pdf]Multi-class motif discovery in keratinocyte differentiation.Daniel Kim.[pdf]Defensive Unit Performance Analysis.Daniel ONeel, Reed Johnson.[pdf]Diagnosing Malignant versus Benign Breast Tumors via Machine Learning Techniques in High Dimensions.Danielle Maddix.[pdf]Hacking the Hivemind.Daria Lamberson,Leo Martel, Simon Zheng.[pdf]Diagnosing Parkinson’s from Gait.Daryl Chang, Marco Alban-Hidalgo, Kevin Hsu.[pdf]Implementing Machine Learning Algorithms on GPUs for Real-Time Traffic Sign Classification.Dashiell Bodington, Eric Greenstein, Matthew Hu.[pdf]Vignette.David Eng, Andrew Lim, Pavitra Rengarajan.[pdf]Machine Learning In JavaScript.David Frankl.[pdf]Searching for exoplanets in the Kepler public data.David Glass, Xiaofan Jin.[pdf]Model Clustering via Group Lasso.David Hallac.[pdf]Improving Positron Emission Tomography Imaging with Machine Learning.David Hsu.[pdf]Algorithmic Trading of Futures via Machine Learning.David Montague.[pdf]Topic based comments exploration for online articles.Deepak Zambre, Ajey Shah.[pdf]Personal Legal Counselor and Interpreter of the Law via Machine Learning.Derek Yan, Tianyi Wang, Patrick Chase.[pdf]Personalized Web Search.Dhanraj Mavilodan, Kapil Jaisinghani, Radhika Bansal.[pdf]Detecting Ads in a Machine Learning Approach.Di Zhang.[pdf]Predicting Mitochondrial tRNA Modification.Diego Calderon.[pdf]Collaborative Neighborhoods.Diego Represas, David Dindi.[pdf]Estimation of Causal Effects from Observational Study of Job Training Program.Dmitry Arkhangelsky, Rob Donnelly.[pdf]Deep Leraning Architecture for Univariate Time Series Forecasting.Dmitry Vengertsev.[pdf]Solomon.Do Kwon, Gyujin Oh, Ki Suk Jang, Ji Park.[pdf]Automatic detection of nanoparticles in tissue sections.Dor Shaviv, Orly Liba.[pdf]Implementation of Deep Convolutional NeuralNet on a DSP.Elaina Chai.[pdf]Evergreen or Ephemeral – Predicting Webpage Longevity Through Relevancy Features.Elaine Zhou, Lingtong Sun.[pdf]MacMalware.Elizabeth Walkup.[pdf]Extractive Fiction Summarization Using Sentence Significance Scoring Models.Eric Holmdahl, Ashkon Farhangi, Lucio Tan.[pdf]Identifying And Predicting Market Reactions To Information Shocks In Commodity Markets.Eric Liu, Vedant Ahluwalia, Deepyaman Datta, Dongyang Zhang.[pdf]An EM-Derived Approach to Blind HRTF Estimation.Eric Schwenker.[pdf]The Many Dimensions of Net Neutrality.Erin Antono, Deger Turan, Justine Zhang.[pdf]Learning To Predict Dental Caries For Preschool Children.Fangzhou Guo, Huaiyang Zhong, Yuchen Li.[pdf]Information based feature selection.Farzan Farnia, Abbas Kazerouni, Afshin Babveyh.[pdf]Identifying Elephant Vocalizations.Flavia Crisrtina Grey Rodriguez, Sergio Patricio Figueroa Sanz.[pdf]Predicting Protein Fragment Binding.Flynn Wu.[pdf]Bike Share Usage Prediction in London.Ford Rylander, Bo Peng, Jeff Wheeler.[pdf]Localized Explicit Semantic Analysis.Francis Lewis.[pdf]Robo Brain Massive Knowledge Base for Robots.Gabriel Kho, Christina Hung, Hugh Cunningham.[pdf]Understanding Music Genre Similarity.Gabriela Groth.[pdf]Correlated Feature Selection for Single-Cell Phenotyping.Geoff Stanley.[pdf]Activity Recognition in Construction Sites Using 3D Accelerometer and Gyrometer.Gustavo Cezar .[pdf]Event-based stock market prediction.Hadi Pouransari, Hamid Chalabi.[pdf]Recommendation Based On User Experience.Hai Vu.[pdf]Spectrum Adaptation in Multicarrier Interference Channels.Haleema Mehmood.[pdf]Exploring Potential for Machine Learning on Data About K-12 Teacher Professional Development.Hamilton Plattner.[pdf]Player Behavior and Optimal Team Compositions for Online Multiplayer Games.Hao Yi Ong, Sunil Deolalikar, Mark Peng.[pdf]Algorithmic Trading Strategy Based On Massive Data Mining.Haoming Li, Tianlun Li, Zhijun Yang.[pdf]Face Detection And Recognition Of Drawn Characters.Herman Chau.[pdf]Gene Expression Analysis Of HCMV Latent Infection.Hie Hong.[pdf]A New Kalman Filter Method.Hojat Ghorbanidehno, Hee Sun Lee.[pdf]Using Tweets for single stock price prediction.Hongshan Chu, Ye Tian, Hongyuan Yuan.[pdf]Classification of Human Posture and Movement Using Accelerometer Data.Huafei Wang, Jennifer Wu.[pdf]Naïve Bayes Classifier And Profitability of Options Gamma Trading.HyungSup Lim.[pdf]Vector-based Sentiment Analysis of Movie Reviews.Ian Roberts, Lisa Yan.[pdf]A General-Purpose Sentence-Level Nonsense Detector.Ian Tenney.[pdf]Characterizing Genetic Variation in Three Southeast Asian Populations.Ilana Arbisser, Jonathan Kang.[pdf]Machine Learning for the Smart Grid.Iliana Voynichka.[pdf]Predicting Africa Soil Properties.Iretiayo Akinola, Thomas Dowd.[pdf]Automated Bitcoin Trading via Machine Learning Algorithms.Isaac Madan, Shaurya Saluja, Aojia Zhao.[pdf]SkatBot.Ivan Leung, Pedro Milani, Ben-han Sung.[pdf]Tradeshift Text Classification.Jacob Conrad Trinidad, Ian Torres.[pdf]New York City Bike Share.James Kunz, Everett Yip, Gary Miguel.[pdf]Predicting Seizure Onset in Epileptic Patients Using Intercranial EEG Recordings.Janet An, Amy Bearman, Catherine Dong.[pdf]Predicting Foster Care Exit.Jason Huang.[pdf]Yelp Recommendation System.Jason Ting, Swaroop Indra Ramaswamy.[pdf]Predicting National Basketball Association Game Winners.Jasper Lin, Logan Short, Vishnu Sundaresan.[pdf]Predicting Yelp Ratings From Business and User Characteristics.Jeff Han, Justin Kuang, Derek Lim.[pdf]Predicting Popularity of Pornography Videos.Jessie Duan.[pdf]Accurate Campaign Targeting Using Classification Algorithms.Jieming Wei, Sharon Zhang.[pdf]Forecasting Bike Rental Demand.Jimmy Du, Rolland He, Zhivko Zhechev.[pdf]Predicting User Following Behavior On Tencent Weibo.Jinfeng Huang, Hai Huang, Zhaoyang Jin .[pdf]Improving Taxi Revenue With Reinforcement Learning.Jingshu Wang, Benjamin Lampert.[pdf]Learning Facial Expressions From an Image.Jithin Thomas, Bhrugurajsinh Chudasama, Chinmay Duvedi.[pdf]All Your Base Are Belong To Us English Texts Written by Non-Native Speakers.Joanna Kim, Jonathan Hung.[pdf]Identifying Regions High Turbidity.Joe Adelson.[pdf]A Comparison of Classification Methods for Expression Quantitative Trait Loci.Joe Davis.[pdf]Predicting Mobile Users Future Location.John Doherty .[pdf]Machine Learning Madness.John Gold, Elliot Chanen.[pdf]Semi-Supervised Learning For Sentiment Analysis.John Miller, Aran Nayebi, Amr Mohamed.[pdf]Legal Issue Spotting.John Phillips.[pdf]A novel way to Soccer Match Prediction.Jongho Shin, Robert Gasparyan.[pdf]Morphological Galaxy Classification.Jordan Duprey, James Kolano.[pdf]Predicting Helpfulness Ratings of Amazon Product Reviews.Jordan Rodak, Minna Xiao, Steven Longoria.[pdf]Predicting Course Completions For Online Courses.Joseph Paetz.[pdf]An Adaptive System For Standardized Test Preparation.Julia Enthoven.[pdf]Single Molecule Biophysics Machine Learning For Automated Data Processing.Junhong Choi, Soomin Cho.[pdf]Understanding Comments Submitted to FCC on Net Neutrality.Junhui Mao, Jing Xia, Woncheol Jeong.[pdf]Direct Data-Driven Methods for Decision Making under Uncertainty.Junjie Qin.[pdf]From Food To Wine.Justin Meier.[pdf]Classifying Legal Questions into Topic Areas Using Machine Learning.Karthik Jagadeesh, Brian Lao.[pdf]Predicting Hit Songs with MIDI Musical Features.Kedao Wang.[pdf]Machine Learning Methods for Biological Data Curation.Kelley Paskov.[pdf]Classifying Forest Cover Type using Cartographic Features.Kevin Crain, Graham Davis.[pdf]Peer Lending Risk Predictor.Kevin Tsai,Sivagami Ramiah,Sudhanshu Singh.[pdf]Learning Distributed Representations of Phrases.Konstantin Lopyrev.[pdf]Estimation Of Word Representations Using Recurrent Neural Networks And Its Application In Generating Business Fingerprints.Kuan Fang.[pdf]Gender Identification by Voice.Kunyu Chen.[pdf]Applications Of Machine Learning To Predict Yelp Ratings.Kyle Carbon, Kacyn Fujii, Prasanth Veerina.[pdf]Methodology for Sparse Classification Learning Arrhythmia.Lee Tanenbaum.[pdf]Predicting March Madness.Levi Franklin.[pdf]Net Neutrality Language Analysis.Li Tao, Xinyi Xie.[pdf]Characterizing Atrial Fibrillation Burden for Stroke Prevention.Lichy Han.[pdf]Predict Seizures in Intracranial EEG Recordings.Linyu He, Lingbin Li.[pdf]Automated Music Track Generation.Louis Eugene, Guillaume Rostaing.[pdf]Characterizing Overlapping Galaxies.Luis Alvarez.[pdf]Understanding Player Positions in the NBA.Luke Lefebure.[pdf]Cross-Domain Product Classification with Deep Learning.Luke de Oliveira, Alfredo Lainez, Akua Abu.[pdf]Predicting Heart Attacks.Luyang Chen, Qi Cao, Sihua Li, Xiao Ju.[pdf]Prediction of Bike Sharing Demand for Casual and Registered Users.Mahmood Alhusseini.[pdf]Classification Of Arrhythmia Using ECG Data.Manas Karandikar, Giulia Guidi.[pdf]What Can You Learn From Accelerometer Data.Manikantan Shila.[pdf]Speaker Recognition for Multi-Source Single-Channel Recordings.Maria Frank, Neil Gallagher, Jose Kruse Perin.[pdf]Prediction of consumer credit risk.Marie-Laure Charpignon, Enguerrand Horel, Flora Tixier.[pdf]Machine Learning for Network Intrusion Detection.Martina Troesch, Ian Walsh.[pdf]Predicting Paper Counts in the Biological Sciences.Matt Denton, Jose Hernandez, Debnil Sur.[pdf]Prediction of Price Increase for MTG Cards.Matt Pawlicki, Joe Polin, Jesse Zhang.[pdf]Twitter Classification into the Amazon Browse Node Hierarchy.Matthew Long, Jiao Yu, Anshul Kundani.[pdf]Determining Mood From Facial Expressions.Matthew Wang, Spencer Yee.[pdf]Visualizing Personalized Cancer Risk Prediction.Maulik Kamdar.[pdf]Predicting the Total Number of Points Scored in NFL Games.Max Flores, Ajay Sohmshetty.[pdf]Short Term Power Forecasting Of Solar PV Systems Using Machine Learning Techniques.Mayukh Samanta,Bharath Srikanth,Jayesh Yerrapragada.[pdf]Star-Galaxy Separation in the Era of Precision Cosmology.Michael Baumer, Noah Kurinsky, Max Zimet.[pdf]Artist Attribution via Song Lyrics.Michael Mara.[pdf]Accelerometer Gesture Recognition.Michael Xie, David Pan.[pdf]Arrythmia Classification for Heart Attack Prediction.Michelle Jin.[pdf]#ML#NLP-Autonomous Tagging Of Stack Overflow Posts.Mihail Eric, Ana Klimovic, Victor Zhong.[pdf]Scheduling Tasks Under Constraints.Mike Yu,Dennis Xu,Kevin Moody.[pdf]Classification Of Beatles Authorship.Miles Bennett, Casey Haaland, Atsu Kobashi.[pdf]Classification of Accents of English Speakers by Native Language.Morgan Bryant, Amanda Chow, Sydney Li.[pdf]Exposing commercial value in social networks matching online communities and businesses.Murali Narasimhan, Camelia Simoiu, Anthony Ward.[pdf]Hacking the genome.Namrata Anand.[pdf]How Hot Will It Get Modeling Scientific Discourse About Literature.Natalie Telis.[pdf]Permeability Prediction of 3-D Binary Segmented Images Using Neural Networks.Nattavadee Srisutthiyakorn.[pdf]Automated Identification of Artist Given Unknown Paintings and Quantification of Artistic Style.Nicholas Dufour, Kyle Griswold, Michael Lublin.[pdf]Predicting Lecture Video Complexity.Nick Su, Ismael Menjivar.[pdf]Result Prediction of Wikipedia Administrator Elections based ondNetwork Features.Nikhil Desai, Raymond Liu, Catherine Mullings.[pdf]Predicting The Treatment Status.Nikolay Doudchenko.[pdf]Error Detection based on neural signals.Nir Even-Chen, Igor Berman.[pdf]Speech Similarity.OReilly Mavrommatis.[pdf]Data-Driven Modeling and Control of an Autonomous Race Car.Ohiremen Dibua, Aman Sinha, and John Subosits.[pdf]Predicting the Diagnosis of Type 2 Diabetes Using Electronic Medical Records.Oliver Bear Dont Walk IV, David Joosten, Tim Moon.[pdf]A Novel Approach to Predicting the Results of NBA Matches.Omid Aryan, Ali Reza Sharafat.[pdf]Automatically Generating Musical Playlists.Paul Martinez.[pdf]Solar Flare Prediction.Paul Warren,Gabriel Bianconi.[pdf]Application of machine learning techniques for well pad identification inathe Bakken oil fielda.Philip Brodrick, Jacob Englander.[pdf]Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods.Phillip Pham,Steven Li.[pdf]Two-step Semi-supervised Approach for Music Structural Classificiation.Prateek Verma, Yang-Kai Lin, Li-Fan Yu.[pdf]Domain specific sentiment analysis using cross-domain data.Praveen Rokkam, Marcello Hasegawa.[pdf]Instrumental Solo Generator.Prithvi Ramakrishnan, Aditya Dev Gupta.[pdf]Cross-Domain Text Understanding in Online SocialData.Qian Lin, Shenxiu Liu, Zhao Yang.[pdf]From Paragraphs to Vectors and Back Again.Qingping He.[pdf]HandwritingRecognition.Quan Nguyen, Maximillian Wang, Le Cheng Fan.[pdf]Chemical Identification with Chemical Sensor Arrays.Quintin Stedman.[pdf]Genre Classification Using Graph Representations of Music.Rachel Mellon, Dan Spaeth, Eric Theis.[pdf]Collaborative Filtering Recommender Systems.Rahul Makhijani, Saleh Samaneh, Megh Mehta.[pdf]Detecting The Direction Of Sound With A Compact Microphone Array.Rajewski.[pdf]Finding Undervalued Stocks With Machine Learning.Ramneet Rekhi, Huan Wei, Tucker Ward, Michael Downs.[pdf]Multilevel Local Search Algorithms for Modularity Clustering.Randolf Rotta, Andreas Noack.[pdf]Automated Detection and Classification of Cardiac Arrhythmias.Richard Tang, Saurabh Vyas.[pdf]Predicting Kidney Cancer Survival From Genomic Data.Rishi Bedi, Duc Nguyen, Christopher Sauer, Benedikt Buenz.[pdf]Multiclass Sentiment Analysis of Movie Reviews.Robert Chan, Michael Wang.[pdf]Classification and Regression Approaches to Predicting US Senate Elections.Rohan Sampath, Yue Teng.[pdf]Learning from Quantified Self Data.Roshan Vidyashankar.[pdf]Predict Influencers in the Social Network.Ruishan Liu, Yang Zhao, Liuyu Zhou.[pdf]Bias Detector.Rush Moody.[pdf]Constructing Personal Networks Through Communication History.Ryan Houlihan, Hayk Matirosyan.[pdf]Modeling Protein Interactions Using Bayesian Networks.Sabeek Pradhan, Shayne Longpre, Varun Vijay.[pdf]Topic Analysis of the FCC’s Public Comments on Net Neutrality.Sachin Padmanabhan, Leon Yao, Luda Zhao, Timothy Lee.[pdf]Predicting Hospital Readmissions.Sajid Zaidi.[pdf]Analyzing Positional Play in Chess Using Machine Learning.Sameep Bagadia, Pranav Jindal, Rohit Mundra.[pdf]Yelp Restaurants’ Open Hours.Samuel Bakouch, Adrien Boch, Benjamin Favreau.[pdf]Identifying Arrhythmia from Electrocardiogram Data.Samuel McCandlish, Taylor Barrella.[pdf]Diagnosing and Segmenting Brain Tumors and Phenotypes using MRI Scans.Samuel Teicher, Alexander Martinez.[pdf]Exploring the Genetic Basis of Congenital Heart Defects.Sanjay Siddhanti, Jordan Hannel, Vineeth Gangaram.[pdf]Attribution of Contested and Anonymous Ancient Greek Works.Sarah Beller, James Spicer.[pdf]Object Detection for Semantic SLAM using Convolutional Neural Networks.Saumitro Dasgupta.[pdf]Sentiment as a Predictor of Wikipedia Editor Activity.Sergio Martinez-Ortuno, Deepak Menghani, Lars Roemheld.[pdf]Blowing Up The Twittersphere- Predicting the Optimal Time to Tweet.Seth Hildick-Smith, Zach Ellison.[pdf]Evergreen Classification_ Exploring New Features.Shailesh Bavadekar.[pdf]Detecting Lane Departures Using Weak Visual Features.Shane Soh, Ella Kim.[pdf]Re-clustering of Constellations through Machine Learning.Shanshan Xu, Kaifeng Chen, Yao Zhou.[pdf]Application of Neural Network In Handwriting Recognition.Shaohan Xu, Qi Wu, Siyuan Zhang.[pdf]Recognition and Classification of Fast Food Images.Shaoyu Lu, Sina Lin, Beibei Wang.[pdf]Reduced Order Greenhouse Gas Flaring Estimation.Sharad Bharadwaj, Sumit Mitra.[pdf]Blood Pressure Detection from PPG.Sharath Ananth.[pdf]Predicting Low Voltage Events on Rural Micro-Grids in Tanzania.Shea Hughes, Samuel Steyer, Natasha Whitney.[pdf]Amazon Employee Access Control System_Updated_Version.Shijian Tang, Jiang Han, Yue Zhang.[pdf]Prediction Onset Epileptic.Shima Alizadeh, Scott Davidson, Ari Frankel.[pdf]Evaluating Pinch Quality of Underactuated Robotic Hands.Shiquan Wang, Hao Jiang.[pdf]Reinforcement Learning With Deeping Learning in Pacman.Shuhui Qu, Tian Tan,Zhihao Zheng.[pdf]Language identification and accent variation detection in spoken language recordings.Shyamal Buch, Jon Gauthier, Arthur Tsang.[pdf]Enhancing Cortana User Experience Using Machine Learning.Siamak Shakeri, Emad Elwany.[pdf]Who Matters.Sid Basu, David Daniels, Anthony Vashevko.[pdf]Predicting Heart Attacks.Sihang Yu, Yue Zhao, Xuyang Zheng.[pdf]Predicting Seizures in Intracranial EEG Recordings.Sining Ma, Jiawei Zhu.[pdf]Structural Health Monitoring in Extreme Events from Machine Learning Perspective.Sophia Zhou,Jingxuan Zhang.[pdf]On-line Kernel Learning for Active Sensor Networks.Stefan Jorgensen.[pdf]ECommerce Sales Prediction Using Listing Keywords.Stephanie Chen.[pdf]Review Scheduling for Maximum Long-Term Retention of Knowledge.Stephen Barnes, Cooper Frye, Khalil Griffin.[pdf]Adaptive Spaced Repetition.Stephen Koo, Sheila Ramaswamy.[pdf]Do a Barrel Roll.Steven Ingram, Tatiana Kuzovleva.[pdf]Oil Field Production using Machine Learning.Sumeet Trehan.[pdf]Predicting Success for Musical Artists through Network and Quantitative Data.Suzanne Stathatos, Zachary Yellin-Flaherty.[pdf]Better Models for Prediction of Bond Prices.Swetava Ganguli, Jared Dunnmon.[pdf]Classifying the Brain 27s Motor Activity via Deep Learning.Tania Morimoto,Sean Sketch.[pdf]Prediction of Bike Rentals.Tanner Gilligan, Jean Kono.[pdf]Classification of Alzheimer’s Disease Based on White Matter Attributes.Tanya Glozman, Rosemary Le.[pdf]MoralMachines- Developing a Crowdsourced Moral Framework for Autonomous Vehicle Decisions.Tara Balakrishnan, Jenny Chen, Tulsee Doshi.[pdf]Context Specific Sequence Preference Of DNA Binding Proteins.Tara Friedrich.[pdf]Predicting Reddit Post Popularity ViaInitial Commentary.Terentiev Tempest.[pdf]Machine Learning for Continuous Human Action Recognition.Tian Tang.[pdf]Predicting Pace Based on Previous Training Runs.Tiffany Jin.[pdf]Probabilistic Driving Models and Lane Change Prediction.Tim Wheeler.[pdf]Multiple Sensor Indoor Mapping Using a Mobile Robot.Timothy Lee.[pdf]Bone Segmentation MRI Scans.Todor Markov William McCloskey.[pdf]#Rechorder Anticipating Music Motifs In Real Time.Tommy Li, Yash Savani, Wilbur Yang.[pdf]Prediction and Classification of Cardiac Arrhythmia.Vasu Gupta, Sharan Srinivasan, Sneha Kudli.[pdf]Predicting DJIA Movements from the Fluctuation of a Subset of Stocks.Veronique Moore.[pdf]Sentiment Analysis for Hotel Reviews.Vikram Elango, Govindrajan Narayanan.[pdf]Mood Detection with Tweets.Wen Zhang, Geng Zhao, Chenye Zhu.[pdf]Comparison of Machine Learning Techniques for Magnetic Resonance Image Analysis.Wendy Ni, Xinwei Shi, Umit Yoruk.[pdf]Object Recognition in Images.Wenqing Yang, Harvey Han.[pdf]3D Scene Retrieval from Text.Will Monroe.[pdf]Predicting Breast Cancer Survival Using Treatment and Patient Factors.William Chen, Henry Wang.[pdf]Parking Occupancy Prediction and Pattern Analysis.Xiao Chen.[pdf]Supervised DeepLearning For MultiClass Image Classification.Xiaodong Zhou.[pdf]User Behaviors Across Domains .Xiaofei Fu, Norman Yu, Abhishek Garg.[pdf]Seizure forecasting.Xiaoying Pang.[pdf]Stock Trend Prediction with Technical Indicators using SVM.Xinjie Di.[pdf]Predicting Usefulness of Yelp Reviews.Xinyue Liu, Michel Schoemaker, Nan Zhang.[pdf]Obstacles Avoidance with Machine Learning Control Methods in Flappy Birds Setting.Yi Shu, Ludong Sun, Miao Yan, Zhijie Zhu.[pdf]Yelp User Rating Prediction.Yifei Feng, Zhengli Sun.[pdf]Demand Prediction of Bicycle Sharing Systems.Yu-chun Yin, Chi-Shuen Lee, Yu-Po Wong.[pdf]Facial Keypoints Detection.Yue Wang,Yang Song.[pdf]Is Beauty Really In The Eye Of The Beholder.Yun (Albee) Ling, Jocelyn Neff, and Jessica Torres.[pdf]Sentiment Analysis of Yelp’s Ratings Based on Text Reviews.Yun Xu, Xinhui Wu, Qinxia Wang.[pdf]Multiclass Classifier Building with Amazon Data to Classify Customer Reviews into Product Categories.Yunzhen Hu, Te Hu, Haier Liu.[pdf]An Energy Efficient Seizure Prediction Algorithm.Zhongnan Fang, Yuan Yuan, Andrew Weitz.[pdf]Classifier Comparisons On Credit Approval Prediction.Zhoutong Fu, Zhedi Liu.[pdf]Appliance Based Model for Energy Consumption Segmentation.Zi Yin, Thanchanok Teeraratkul, Nutthavuth Tamang.[pdf]analysis on 1s1r array.Zizhen Jiang.[pdf]Video Series:https://www.youtube.com/watch?v=p4FR37aJSKchttps://www.youtube.com/watch?v=5B80x26K8cQhttps://www.youtube.com/watch?v=86lUBfVMe24https://www.youtube.com/watch?v=Yceqk8vmXPMhttps://www.youtube.com/watch?v=BBwEF6WBUQshttps://www.youtube.com/watch?v=fbAdS063bk4https://www.youtube.com/watch?v=svz28L6Ay_chttps://www.youtube.com/watch?v=Ns4aJ-DfoKYhttps://www.youtube.com/watch?v=HF6um7Txmh4https://www.youtube.com/watch?v=eJd3PUhj3Nshttps://www.youtube.com/watch?v=j11fYpycAhkhttps://www.youtube.com/watch?v=LdNYz5YTLhUhttps://www.youtube.com/watch?v=5TkX1mX7elEhttps://www.youtube.com/watch?v=ZX2Hyu5WoFghttps://www.youtube.com/watch?v=cTrG81H08Bkhttps://www.youtube.com/watch?v=vOEushDQyj0https://www.youtube.com/watch?v=qrHhO9E4lIshttps://www.youtube.com/watch?v=G8_PUk50qpchttps://www.youtube.com/watch?v=_X7HfmN63r4https://www.youtube.com/watch?v=N3BJDnDNuVUhttps://www.youtube.com/watch?v=4e3NvB0nc2Mhttps://www.youtube.com/watch?v=lTPO8rrzracFurther 35 Project Ideas or Suggestions which might interest you.1. AI (Artifical Intelligence) Based Image Capturing and transferring to PC/CCTV using Robot2. Material Dimensions Analyzing Robot3. An intelligent mobile robot navigation technique using RFID Technology4. IVRS Based Robot Control with Response & Feed Back5. Library Robot – Path Guiding Robotic System with Artificial Intelligence using Microcontroller6. Wireless Artificial Intelligence Based Fire Fighting Robot for Relief Operations7. A Humanoid Robot to Prevent Children Accidents8. Motion Detection, Robotics Guidance & Proximity Sensing using Ultrasonic Technology9. Robust Sensor-Based Navigation for Mobile Robots10. Visual tracking control to fast moving target for stereo vision robot11. A Voice Guiding System for Autonomous Robots12. Artificial Intelligent based Solar Vehicle13. Mobile robot control based on information of the scanning laser range sensor14. Walking Robot with Infrared Sensors / Light Sensors / RF Sensor / Tactile Sensors15. IVRS Based Control of Three Axis Robot With Voice Feed back16. Intelligent Mobile Robot for Multi Specialty Operations17. Sensor Operated Path Finding Robot (Way Searching)18. Design and Development of Obstacle Sensing and Object Guiding Robot19. SMS controlled intelligent searching and pick and place moving robot20. Artificial Intelligence Based Image Capturing and Transferring to PC using Robot21. Artificial Intelligent Based Remote controlled Automatic Path finding Cum Video Analyzing Robot22. Wall Follower Robot with The Help of Multiple Artificial Eyes23. Sensor Operated Automatic Punching robot24. Fire Fighting Robotics with AI (Artificial Intelligence) and WAP25. Intelligent Robot with Artificial Intelligence computer Brain system26. Remote controlled Pneumatic Four Axis Material Handling Robot27. Advanced Robotic Pick and Place Arm and Hand System28. Voice Controlled Material handling Robot29. A Hands Gesture Control System of for an Intelligent Robot30. Robotic Vision and Color Identification System with Solenoid Arm for Colored Material Separation31. Artificial Intelligence Based Fire fighting AGV32. SMS controlled video analyzing robot33. Staircase Climbing Robot – Implemented in Multi-Domain Approach34. Fully Automated Track Guided Vehicle (ATGV) Robot35. PC based wireless Pick and Place jumping robot with remote control36. Three Axis Robotics With Artificial Intelligence (AI)

Top 10 Cyber Security breaches of 2015[Image Credit: Logic Works]Data breaches have become a status quo considering how attackers keep finding paths to infiltrate networks and steal confidential information. Last year, we have seen big industry breaches such as Sony, JP Morgan Chase, Target, eBay etc. This year hasn’t changed much. The security industry has seen not just targeted attacks at these organizations but also there is this theme around the nation-state-sponsored hackers because they are generally resourced the best, and their collective motivations run across the spectrum. While the security breach barrage on one end continues, investments are pouring into security technologies on the other end and it’s clearly not enough.Here are the top 10 cyber security breaches of 2015 categorized from least to most compromised records.10. SlackWhen it happened: March 2015No of records compromised: 500,000 email addresses and other personal account data (phone number, Skype ID, etc)Slack’s blog confirmed that Slack’s hashing function is bcrypt with a randomly generated salt per-password. We have seen so many unauthorized database incidents before. Haven’t we? Think about HipChat and Twitch. It was not too long before they experienced similar breach.Lesson Learned: For companies that are still relying on passwords, it’s a blow. Do not just use salting. Invest in technologies and people to prevent hackers getting access to your database in the first place. Overcome the post-breach mindset.9. Hacking TeamWhen it happened: July 2015No of records compromised: 1 million emailsThe Hacking Team develops spy tools for government agencies, including those that can go around traditional anti-virus solutions.This breach published more than 1 million emails from the Italian surveillance company, revealing its involvement with oppressive governments as well as multiple Flash zero-day vulnerabilities and Adobe exploits. As a cyber security professional, this is definitely frightening. A full list of Hacking Team's customers were leaked in the 2015 breach that included mostly military, police, federal and provincial governments.Lesson Learned: Patch your systems and applications. Inventory your systems and applications. This has been extensively covered as part of NIST SP-800-137, SANS CSC and ASD.8. KasperskyWhen it happened: June 2015No of records compromised: Affected multiple customersKaspersky blog reported that “We’ve found that the group behind Duqu 2.0 also spied on several prominent targets, including participants in the international negotiations on Iran’s nuclear program and in the 70th anniversary event of the liberation of Auschwitz”.If you don’t know about Duqu, it’s sometimes referred to as the stepbrother of Stuxnet. One of the most notable features of Duqu 2.0 was its lack of persistence, leaving almost no traces in the system. The malware made no changes to the disk or system settings: the malware platform was designed in such a way that it survives almost exclusively in the memory of infected systems. The technical details about this are published here.Kaspersky’s breach just proves that some of the security-conscious organizations can fall victim to determined hackers.Lessons Learned: Security teams have to adopt this as part of continuous monitoring strategy. Know your network. Train your teams to alert for any suspicious activity on the network. Do not just monitor inbound communications. Be watchful of all the security updates as a general best practice.7. CareFirst BlueCross BlueShieldWhen it happened: May 2015No of records compromised: 1.1 million records1.1 million members had their names, birth dates, email addresses and subscriber information compromised, but member password encryption prevented cybercriminals from gaining access to Social Security numbers, medical claims, employment, credit card and financial data.CareFirst discovered the breach as part of a Mandiant-led security review that found hackers had gained access to a database that members use to get access to the company's website and servicesLesson Learned: Enable DNS query logging to detect hostname lookup for known malicious C2 domains. Detect random string entropy - unknown certificates, file names etc. Disclose and communicate data breaches in a timely manner.6. LastPassWhen it happened: July 2015No of records compromised: 7 million usersThe password management company LastPass revealed that it had been the victim of a cyberattack, compromising email addresses, password reminders, server per user salts and authentication hashes. “LastPass strengthens the authentication hash with a random salt and 100,000 rounds of server-side PBKDF2-SHA256, in addition to the rounds performed client-side. This additional strengthening makes it difficult to attack the stolen hashes with any significant speed”, the company said.Salts are really not useful for preventing dictionary attacks or brute force attacks. One of the drawbacks of the hashing algorithm PBKDF2-SHA256 employed by LastPass is that it was not designed to protect passwords.Lesson Learned: For end users, make sure you rotate master passwords periodically. Also ensure that you have password reminders/recovery questions different for every critical application.5. Premera BlueCross BlueShieldWhen it happened: March 2015No of records compromised: 11.2 million recordsPremera BlueCross BlueShield said in March that it had discovered a breach in January that affected as many as 11.2 million subscribers, as well as some individuals who do business with the company. The breach compromised subscriber data, which includes names, birth dates, Social Security numbers, bank account information, addresses and other information. There were suits filed against Premera for waiting roughly six weeks to tell victims that their data might have been exposed. Pile of lawsuits filed against Premera— for being negligent, breached its contract with customers, violated the Washington Consumer Protection Act and failed to disclose the breach in a timely manner.ThreatConnect blog indicates that the prennera[.]com domain may have been impersonating the Healthcare provider Premera Blue Cross, where the attackers used the same character replacement technique by replacing the “m” with two “n” characters within the faux domain.It definitely looks like suspicious domain, prennera.com which is likely a spoof of Premera, and a malicious payload signed with the same digital certificate as malware from the Anthem hack.Lesson Learned: Enable DNS query logging to detect hostname lookup for known malicious C2 domains. Detect random string entropy - unknown certificates, file names etc. Monitor for overly short certificates, certificates with missing information, etc. Disclose and communicate data breaches in a timely manner.4. Experian/T-MobileWhen it happened: October 2015No of records compromised: 15 million people’s recordsT-Mobile uses Experian to process its credit applications. Experian Plc (EXPN.L), the world's biggest consumer credit monitoring firm disclosed a massive data breach that exposed sensitive personal data of some 15 million people who applied for service with T-Mobile US Inc.Experian explained the details on its Web site:The unauthorized access was in an isolated incident over a limited period of time. It included access to a server that contained personal information for consumers who applied for T-Mobile USA postpaid services or products, which require a credit check, from Sept. 1, 2013 through Sept. 16, 2015.Brian Krebs reported in his blog that the Experian’s Decision Analysis credit information support portal allowed anyone to upload arbitrary file attachments of virtually any file type. Those experts said such file upload capabilities are notoriously easy for attackers to use to inject malicious files into databases and other computing environments, and that having such capability out in the open without at least first requiring users to supply valid username and password credentials is asking for trouble. Experian’s insecurity has dragged T-Mobile into its privacy scandal.Lesson Learned: Bake security assessment as part of acquisition strategy. Also, do not open systems exposed to internet without any form of authentication.3. Office of Personnel ManagementWhen it happened: June 2015No of records compromised: 21-25 million federal workers records (including both breaches)On Sep23, OPM Press Secretary Sam Schumach stated that “Of the 21.5 million individuals whose Social Security Numbers and other sensitive information were impacted by the breach, the subset of individuals whose fingerprints have been stolen has increased from a total of approximately 1.1 million to approximately 5.6 million”.These kind of breaches involving biometric data like fingerprints are unique and particularly concerning because you cannot rotate these unlike passwords. These are permanent identity of those people.A report (PDF) by OPM’s Office of the Inspector General on the agency’s compliance with FISMA finds “significant” deficiencies in the department’s IT security. The report found OPM did not maintain a comprehensive inventory of servers, databases and network devices, nor were auditors able to tell if OPM even had a vulnerability scanning program. The audit also found that multi-factor authentication (the use of a token such as a smart card, along with an access code) was not required to access OPM systems. “We believe that the volume and sensitivity of OPM systems that are operating without an active Authorization represents a material weakness in the internal control structure of the agency’s IT security program,” the report concluded.Lesson Learned: Implement multi-factor authentication for admins accessing sensitive systems, implement continous monitoring strategy. It is important to constantly fine-tune your logs and baseline your environment.2. Ashley MadisonWhen it happened: July 2015No of records compromised: 37 million clientele recordsAshley Madison made headline after a hacking group, the Impact team penetrated its servers and published the information of all 37 million users online.The hackers leaked maps of sensitive information - including internal company servers, employee network account information, company bank account data and salary information. According to security consultant Gabor Szathmari, Ashley Madison may have made things easy for their attackers by writing a variety of credentials directly into their source code -- including database credentials, SSL private keys, Twitter OAuth tokens, and Amazon Web Services credentials.In addition, the database passwords Szathmari found "were between 5 and 8 characters, and many of them contained 2 character classes only.” Aside from hardcoded credentials, Szathmari also noted that the website didn't employ form or email validation to help screen out bots.Lesson learned: Never ever store clear-text sensitive data in your source code, rotate your API tokens and service credentials. Educate software developers about secure coding practices1. AnthemWhen it happened: Feb 2015No of records compromised: 80 million patient and employee recordsThe breach was revealed in February that exposed an astonishing 80 million patient and employee records. Anthem said the breach exposed names, date of birth, Social Security numbers, health-care ID numbers, home addresses, email addresses, employment information, income data and more. The attack would not have been possible if Anthem had ensured that data at rest was securely encrypted and as a result, millions of peoples’ confidential information would not be in the hands of the hackers.Derusbi is a family of malware used by multiple actor groups but associated exclusively with Chinese APT as part of Anthem breach.ThreatConnect blog indicates that the “Sakula” (aka. Sakurel) family of malware, a known variant of the Derusbi backdoor, and was configured to communicate with the malicious command and control (C2) domains extcitrix.we11point[.]com and www.we11point[.]com. They also confirmed that this malicious infrastructure was likely named in such a way to impersonate the legitimate Wellpoint IT infrastructure.Lesson learned: Do not just rely on perimeter security. Use a threat intelligence platform to be able to recognize potential malware activity from multiple threat intelligence sources and act upon. Encrypt data-at rest and ensure that the encryption keys, network access control and identity management all work together to ensure data is secure.In 2016, attacks are only going to get worse and we need to step up our game rather than just relying on tools. More security vendors will be targeted, drones hacked, ERP platforms continuing to be used as conduits to cause real-world physical damage by attacking industrial control systems, more darknets and blackmarkets surge and more nation-sponsored attacks to come.

There’s an awesome book I have to suggest called Debt: The First 5000 Years, by David Graber. It’s available in PDF form free at the link, but I’ll warn you now, it’s a long, in-depth, and intensely intellectual treatise. I’ll sum it up in as brief a manner as possible.Debt is the fundamental unit of human transaction. When someone does something for you and you do not immediately do something of similar value for them, you create a debt. Debt pre-existed markets, it pre-existed currency, and it pre-existed barter, which was long thought to be the earliest form of exchange.In the earliest societies, debt was considered a moral obligation — if you didn’t pay your debts, said every religion ever, you were a bad person. This was closely tied to the goal of every religion ever, which was to keep people from being assholes to each other. Paying back your debts was considered a chief way to not be an asshole, and thus, it was morally correct. On an individual human level, this is a basic fact of our everyday lives. (This is also why many religions outlawed the charging of interest, as it was seen as immoral to increase other people’s debts for no reason other than your own profit.)The historical records show, however, that literally most our very earliest writings were attempts to codify the value of things in an abstracted form so that we could arrange for complex, multi-party exchanges. Hammurabi’s famous code of laws? Yeah, that was mostly a massive list of the value of things for the purposes of establishing the size of a debt owed.So society advanced, and debt turned into currency (and thus into markets) when states rose up that were capable of enforcing a unit of currency (i.e. forcing merchants to accept it as tender for debt and forcing employers to pay workers in it). As debts became abstracted in the form of amounts of currency, they shifted from vague moral obligations to matters of strict accountancy.But of course, bad things happen to people, and some bad things aren’t the people’s faults, so most ancient societies (including Hammurabi’s) included some form of the Jubilee, a once-every-seven-years ceremony that literally erased all debts owed to entities (i.e. temples, the government, banks) rather than specific individual humans. This prevented people from becoming lifelong debt-slaves or debt-prisoners.The problem with debt in the modern sense arose with the invention of advertising. Advertising, starting in its earliest widespread form in the newspapers of 18th-century England, was custom-built to convince people to do things that would put them in debt, even if it meant exaggerating, bullying, or lying to get the job done.Alone, advertising wasn’t really enough to cause major problems with debt, however — it wasn’t until the 1920s when mass advertising of a new wave of consumer appliances (most famously washing machines) met up with the advent of mass consumer credit (in the form of installment payments). When it became abundantly clear (in the Great Depression) that consumers had been essentially tricked into owing far more than they could afford, the question of whether or not debt was actually a legitimate concept arose anew.In short, a hundred years ago (and across all of time before that), debt was absolutely a real thing in the same way that ‘justice’ and ‘citizenship’ were real things. Today, however, we’re realizing that there is a huge divorce between the old concept of debt as morality (wherein debt was primarily incurred voluntarily and without deceit) and the modern reality of debt (which is incurred under enormous psychological pressures from advertisers as well as complex factors such as the application of retail psychology that are deliberately kept away from the public’s consciousness).Essentially, we have to decide whether advertising, retail psychology, the credit card, and other gambits used by the commercial world to dupe us into spending money we wouldn’t normally are equivalent to false advertising (thus rendering the debts incurred under them invalid), or whether it’s up to every individual consumer to become aware of and learn how to resist those influences. Caveat emptor!