CSC 4510/9010: Machine Learning
Dr. Paula Matuszek
Adjunct Professor, Villanova
E-mail: Paula.Matuszek@villanova.edu or Paula.Matuszek@gmail.com
Phone: (610) 647-9789
TA: Kambagiri Atte, email@example.com
Machine learning is a fast-moving field, and the syllabus may be modified significantly as the semester progresses.
Jan 13: Intro, architecture of a learning system, inputs
Readings: Chapters 1, 2, 10, pp 407-411 of chapter 11.
Jan 20: Outputs. Supervised learning: Decision Trees, ARFF Format, Piazza.
Readings: Chapters 3.3, 4.3, , pp 416-419 of chapter 11, 17.1.
Jan 27: Supervised Learning 2: Instance-Based Learning.
Evaluation, interpretation. Grad paper presentation.
Readings: Chapters 3.5, 4.7, 17.2.
Feb 3: Supervised Learning 3: Rules, Weka Visualization
Readings: Chapters 3.4, 4.1, 4.4, 17.3.
Note that assignments are now getting posted on, and submitted through, Blackboard. Assignment 3 is posted, due Feb 10.
Feb 10: Supervised Learning 4: Naive Bayes, Applying Weka.
Readings: Chapters 3.2, 4.2.
Student presentation: RajaHarish Vempati. Machine Learning for Pedestrian Detection in Smart Driver Assistance Systems Presentation
Note that assignments are now getting posted on, and submitted through, Blackboard. Assignment 4 is posted, due Feb 17.
Presentation Information (CSC 9010 only) Note that presentation materials should be submitted through Blackboard.
Feb 17: Supervised Learning 5: Linear and Logistic Regression, SVMs.
Readings: Chapters 3.2, 4.6, 11.4.
Student presentation: Bharadwaj Vadlamannati. Applying machine learning to steganalysis Presentation
Feb 24: Midterm.
Mar 3: Spring break
Mar 10: Neural Nets.
Readings: Chapter 6.4, pp 232-241.
Student presentation: Nikhil Dasari. Gesture Recognition around Mobile Devices.
Mar 17: Guest Speaker: Dr. Cassel.
Neural Nets Lab
Student presentation: Sruthi Moola. Convolutional Neural networks for image processing with applications in mobile robotics Presentation
Note that assignments are now getting posted on, and submitted through, Blackboard. Assignment 5 was posted last week, due Mar 21.
Mar 24: Unsupervised Learning: Clustering.
Student presentation: Gopi Krishna Chitluri. Classification of ciphers using Machine Learning Presentation
Mar 31:Interpreting Clustering Results
Interpreting Clusters Lab
Pradeep Musku: Machine Learning in Stock Price Trend Forecasting, Presentation
Christine Fossaseca, Machine Learning as Applied to Intrusion Detection Presentation
Apr 7: Unsupervised Learning: Dimensionality reduction, SOMs.
Unsupervised Presentation 2
Student presentation: Sai Koushik Haddunoori. Machne Learning for Spam Filtering. Presentation
Apr 14: Other kinds of learning: Inductive, active, grounded language,
ontology-based. Cynthia Matuszek, guest speaker.
Guest Speaker Presentation Red Tape
Apr 21: Other kinds of learning: Reinforcement,
transfer learning. Summary, Conclusions.
Red Tape Reinforcement and Transfer Learning Summary
Apr 28: Project Reports.
May 5: Final. Time is 6:15-9:00, same room as class.