A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | AA | |
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1 | Name | Co-Coach / Company | Download ? | ||||||||||||||||||||||||
2 | |||||||||||||||||||||||||||
3 | Course Projects for Cmput 466/566 [Jan-Apr'19] | ||||||||||||||||||||||||||
4 | Forecasting Rec. Facility Turnout Across Edmonton | Roman Eisner <roman.eisner@edmonton.ca> (City of Edmonton) | |||||||||||||||||||||||||
5 | Natural Learning Processing Predicting Evolution & Success of Slang | Shaheer Rizvi <sarizvi@ualberta.ca> Paul Messinger <paulm@ualberta.ca> | |||||||||||||||||||||||||
6 | Brain-Age Classification | Bo Cao <bcao2@ualberta.ca> | |||||||||||||||||||||||||
7 | Sex classification based on brain | Bo Cao <bcao2@ualberta.ca> | |||||||||||||||||||||||||
8 | Predicting Wildfire Initial Attack Success in Alberta | Ilbin Lee <Ilbin@ualberta.ca> | |||||||||||||||||||||||||
9 | Predicting 30 day mortality
| Ram Anantha Ram.Anantha@albertahealthservices.ca | |||||||||||||||||||||||||
10 | Predicting brain hematoma expansion in stroke patients | Brian Buck <bbuck@ualberta.ca> | |||||||||||||||||||||||||
11 | Segmenting hematoma in stroke patients | Brian Buck <bbuck@ualberta.ca> | |||||||||||||||||||||||||
12 | Using machine learning to detect anomaly in transactions | Gunjan Kaur <gkaur2@atb.com>, Dmitriy Volinskiy <dvolinskiy@atb.com> (ATB) | |||||||||||||||||||||||||
13 | Detecting Anomaly of Customer’s Banking Activity | Gunjan Kaur <gkaur2@atb.com>, Dmitriy Volinskiy <dvolinskiy@atb.com> | |||||||||||||||||||||||||
14 | Image classification on attendance and numbers of P Falcon | E Hedlin <hedlin@ualberta.ca> | |||||||||||||||||||||||||
15 | Convolutional Neural Networks in Ultrasound Image Segmentation | Abhilash Rakkunedeth <abhilash@medo.ai> Medo.AI | |||||||||||||||||||||||||
16 | |||||||||||||||||||||||||||
17 | Course Projects for Cmput 466/551 [Sept-Dec'16] | ||||||||||||||||||||||||||
18 | Oil Price Forecasting Using Machine Learning | Craig Schram, Finbarr Timbers Darkhorse Analytics | |||||||||||||||||||||||||
19 | Automated Image Segmentation for Retinal Images of subjects diagnosed with Choroideremia disease | Dana Cobzas John Dimopoulos | |||||||||||||||||||||||||
20 | Learning maternal characters to predict offspring health | Padma Kaul | |||||||||||||||||||||||||
21 | Instrument Transcription (Recognize the pitches in music) | Greg Burlet Frettable | |||||||||||||||||||||||||
22 | Nonparametric Bayesian Tensor Factorization | Junfeng Wen | |||||||||||||||||||||||||
23 | Machine Learning in Real-time Heuristic Search | Vadim Bulitko | |||||||||||||||||||||||||
24 | Multitask Learning For Crime Prediction in the Edmonton Area | Koosha Golmohammadi / City of Edmonton | |||||||||||||||||||||||||
25 | Otto Group Product Classification Challenge | Peng Xu | |||||||||||||||||||||||||
26 | Gender prediction from handwriting | Peng Xu | |||||||||||||||||||||||||
27 | Predicting Potential Subscribers Based on Jobber Free Trial Accounts | Roman Eisner Jobber | |||||||||||||||||||||||||
28 | Predicting Customer Churn | Roman Eisner Jobber | |||||||||||||||||||||||||
29 | Optimizing L2 for Patient-Specific Survival Prediction | Bret Hoehn | |||||||||||||||||||||||||
30 | Using backoff and stacking methods in Survival Prediction | Negar Hassanpour | |||||||||||||||||||||||||
31 | Automated Bird Species Recognition in Noisy Environments | Vadim Bulitko, Erin Bayne | |||||||||||||||||||||||||
32 | |||||||||||||||||||||||||||
33 | Course Projects for Cmput 466/551 [Sept-Dec'15] | ||||||||||||||||||||||||||
34 | Multiple Sclerosis Lesion Segmentation in MRI Images | D Cobzas | |||||||||||||||||||||||||
35 | Analysis of feature learning on image segmentation | D Cobzas | |||||||||||||||||||||||||
36 | Machine Learning: A *Gaitway* to Effective Pedobarometer Analysis | Albert Vette | |||||||||||||||||||||||||
37 | Outcome Prediction in the Game of Curling | ||||||||||||||||||||||||||
38 | Predicting NMR Chemical Shifts from Chemical Structures | D Wishart | |||||||||||||||||||||||||
39 | Modeling lung cancer patient survival data obtained from SEER using MTLR method | Negar Hassanpour | |||||||||||||||||||||||||
40 | Mixed-norm regularization for Patient Specific Survival Prediction | ||||||||||||||||||||||||||
41 | Analyzing Housing Prices and Predicting Sale Values | Koosha Golmohammadi / City of Edmonton | http://ace.edmonton.ca/machine-learning/ | ||||||||||||||||||||||||
42 | Predicting Bus Arrival Time | Koosha Golmohammadi / City of Edmonton | http://ace.edmonton.ca/machine-learning/ | ||||||||||||||||||||||||
43 | A Semi-supervised approach to disaggregate household energy consumption data | C Szespesvari | |||||||||||||||||||||||||
44 | Metadata Retrieval on Music Files | A Hindle | |||||||||||||||||||||||||
45 | Elucidating energy consumption on mobile platforms using current machine learning techniques. | A Hindle | |||||||||||||||||||||||||
46 | How to wreck a nice tweet? | Erin Bayne | |||||||||||||||||||||||||
47 | |||||||||||||||||||||||||||
48 | Course Projects for Cmput 466/551 [Sept-Dec'14] | ||||||||||||||||||||||||||
49 | T01: Classification of Subsequent Memory Effect Using EEG Data & Machine Learning | Matt Brown | Download | ||||||||||||||||||||||||
50 | T02: Handling Missing Data in the Multi-task LogisticRegression Model | Ping Jin | |||||||||||||||||||||||||
51 | T03: Machine learning for mountain pine beetle | Mark Lewis | |||||||||||||||||||||||||
52 | T04: Mixture of Experts Model for Image Splicing Detection | Nilanjan Ray | |||||||||||||||||||||||||
53 | T05: Classification of Protein Crystallization | Lu Deng | |||||||||||||||||||||||||
54 | T06: Colonic Polyp Prediction based on Urinary Metabolites | Roman Eisner Metabolomics Technology Inc. (MTI) | |||||||||||||||||||||||||
55 | T07: Project Smiley :-) | ||||||||||||||||||||||||||
56 | T08: Sentiment Analysis In Figurative Language (using Twitter Data) | Greg Kondrak | |||||||||||||||||||||||||
57 | T09: Comparing linear and non-linear methods for feature selection and classification in EEG motor imagery tasks | Patrick Pilarski | |||||||||||||||||||||||||
58 | T10: Bear Classification and Detection | Mark Lewis | |||||||||||||||||||||||||
59 | T11: Modeling Software Energy Consumption: A Machine Learning Approach | Abram Hindle | |||||||||||||||||||||||||
60 | T12: Sentiment Analysis on Movie Reviews (Kaggle) | Ameneh Gholipour Shahraki, Ali Yadollahi | |||||||||||||||||||||||||
61 | T13: Epileptic Seizure prediction | Matt Brown | |||||||||||||||||||||||||
62 | |||||||||||||||||||||||||||
63 | Course Projects for Cmput 466/551 [Sept-Dec'13] | ||||||||||||||||||||||||||
64 | Create an algorithm to distinguish dogs from cats | ||||||||||||||||||||||||||
65 | Towards EMG-based Robot Arm Control: Motion Primitives and Classification | Patrick Pilarski | |||||||||||||||||||||||||
66 | Efficiently finding Discriminatory Genes | Sheehan Khan | |||||||||||||||||||||||||
67 | Evaluation of Music Feature Extraction Methods for Artist Identification and Track Generation | Greg Burlett | |||||||||||||||||||||||||
68 | Reverse Metronome | Greg Burlett | |||||||||||||||||||||||||
69 | Survival prediction | Ping Jin | Download | ||||||||||||||||||||||||
70 | Using SNPs to Select Maize Lineages for Breeding | Christina Liu, Mohsen Hajiloo | |||||||||||||||||||||||||
71 | Finding Recoverability Number For Oil From Oil Sand Ore | ||||||||||||||||||||||||||
72 | Applying machine learning techniques to diagnosis of ADHD from fMRI data | ||||||||||||||||||||||||||
73 | Learning to rank hotels | Bret Hoehn | |||||||||||||||||||||||||
74 | Predicting Prognosis for Brain Tumor Patients | Bret Hoehn | |||||||||||||||||||||||||
75 | Detecting Duplicate Bug Reports | Abram Hindle | Download | ||||||||||||||||||||||||
76 | |||||||||||||||||||||||||||
77 | Course Projects for Cmput 466/551 [Jan-Apr'08] | ||||||||||||||||||||||||||
78 | Extraction of Additional Data to Improve Netflix Prediction Accuracy (Netflix) | Bret Hoehn | |||||||||||||||||||||||||
79 | Using reinforcement learning to learn parameters for gradient descent | ||||||||||||||||||||||||||
80 | Movie Classification (Netflix) | Bret Hoehn | |||||||||||||||||||||||||
81 | Combining Predictors (Netflix-ish) | Bret Hoehn | |||||||||||||||||||||||||
82 | An Investigation of the Effect of Using Additional Movie Information in Netflix Prize (Netflix) | Bret Hoehn | |||||||||||||||||||||||||
83 | Learning the Betting Abstraction of Computer Agents in No-Limit Texas Hold'em Poker | ||||||||||||||||||||||||||
84 | Soft sensor developments for chemical processes using machine learning methods | ||||||||||||||||||||||||||
85 | Machine Learning on Metabolomics Datasets | Roman Eisner | |||||||||||||||||||||||||
86 | Investigating Clustering and Feature Selection methods for Microarray Data Analysis | Barnabas Pocnoz | |||||||||||||||||||||||||
87 | Discovering Regularities in Sparse Representation data | Barnabas Pocnoz | |||||||||||||||||||||||||
88 | |||||||||||||||||||||||||||
89 | Course Projects for Cmput 466/551 [Jan-Apr'05] | ||||||||||||||||||||||||||
90 | MLCV (Machine Learning for Computer Vision) | ||||||||||||||||||||||||||
91 | Comparison of Anomaly Detection Methods for Disease Outbreak Analysis | ||||||||||||||||||||||||||
92 | Recognizing and acting on hand signals using vision on an AIBO | ||||||||||||||||||||||||||
93 | Creature Motor Control With Reinforcement Learning | ||||||||||||||||||||||||||
94 | Supervised Machine Learning of Movie Reviews for Polarity Determination, Genre Categorization and Success Prediction | ||||||||||||||||||||||||||
95 | Learning Models of Exercise Response | ||||||||||||||||||||||||||
96 | Automated Heuristic Refinement applied to Sokoban | ||||||||||||||||||||||||||
97 | Opponent Modeling In "President" | ||||||||||||||||||||||||||
98 | Transmembrane Region Prediction | ||||||||||||||||||||||||||
99 | Learning better evaluation functions by implementing TDLeaf in Checkers | ||||||||||||||||||||||||||
100 |