CSC 4510/9010: Machine Learning
Spring, 2015
Dr. Paula Matuszek
Adjunct Professor, Villanova
E-mail: or
Phone: (610) 647-9789
TA: Kambagiri Atte,


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 Presentation.  Weka.  Assignment 1.
                 Readings: Chapters 1, 2, 10, pp 407-411 of chapter 11.

Jan 20:   Outputs. Supervised learning: Decision Trees, ARFF Format, Piazza. Presentation   Assignment 2.
                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. Presentation  
                Readings: Chapters 3.5, 4.7, 17.2.

Feb 3:    Supervised Learning 3: Rules, Weka Visualization Presentation   Lab  
                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. 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. Regression Presentation   Lab  
                 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.   NN Presentation  
                 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.  Clustering presentation  
                 Student presentation: Gopi Krishna Chitluri. Classification of ciphers using Machine Learning Presentation  

Mar 31:Interpreting Clustering Results    Interpreting Clusters Lab
                 Student presentations:
                 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 Lab/Homework  
                 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.