Course Calendar/Schedule (Subject to change)

Week Day  Date Topic Background Reading Assigned     Due Points
1 Wed 21-Sep-16 Concept Learning, Decision Trees Machine Learning: Chapter 3: Decision Trees
1 Fri 23-Sep-16 Python 2.7 & Decision Trees   HW 1: Decision Trees   10
2 Mon 26-Sep-16 Measuring Distance  The wikipedia article on distance
2 Wed 28-Sep-16 Instance-based Learning (K-nearest neighbor) The String-to-string Correction Problem
2 Fri 30-Sep-16 More on KNN and distance measures Elements of Statistical Learning: 2.3. & 13.3 HW 2: K-nearest neighbor HW 1 10
3 Mon 3-Oct-16 Linear regression Elements of Statistical Learning: Chapter 3
3 Wed 5-Oct-16 Linear discriminants Elements of Statistical Learning: Chapter 4 (focus on 4.5)
3 Fri 7-Oct-16 Recitation A non-mathy explanation of Principal Component Analysis HW 3: Linear regression HW 2 10
4 Mon 10-Oct-16 Support Vector Machines Elements of Statistical Learning: Chapter 12
4 Wed 12-Oct-16 Kernels  Kernel Methods in Machine Learning
4 Fri 18-Oct-16 Midterm Preparation A tutorial on SVMs   HW 3  
5 Mon 17-Oct-16 MIDTERM MIDTERM MIDTERM 10
5 Wed 19-Oct-16 Collaborative Filtering Recommender Systems: Chapter 2
5 Fri 21-Oct-16 Midterms returned   HW 4: Collaborative Filters   10
6 Mon 24-Oct-16 Hypothesis Testing Machine Learning: Chapter 5: Evaluating Hypotheses
6 Wed 26-Oct-16 Naive Baysian Classifiers Elements of Statistical Learning: Chapter 7
6 Fri 28-Oct-16 Recitation   HW 5: Bayes Classifier HW 4 10
7 Mon 31-Oct-16 Expectation Maximization Elements of Statistical Learning: Chapter 8.5
7 Wed 2-Nov-16 Gaussian Mixture Models Elements of Statistical Learning: Chapter 6.8
7 Fri 4-Nov-16 Recitation Machine Learning: Chapter 4: Neural Networks HW 6: Gaussian Mixture Model HW 5 10
8 Mon 7-Nov-16 Neural Networks: Perceptrons Scaling Learning Algorithms towards AI
8 Wed 9-Nov-16 Neural Networks: Multilayer Perceptrons Learning Deep Architectures for AI
8 Fri 11-Nov-16 Tensor Flow Tensor Flow HW 7: Neural Networks HW 6 10
9 Mon 14-Nov-16 Neural Networks: Restricted Boltzman Dropout: A simple way to prevent overfitting
9 Wed 16-Nov-16 Neural Networks: Deep Belief Networks
9 Fri 18-Nov-16 scikit-learn scikit-learn homepage HW 8: Boosting & Active Learning 10
10 Mon 21-Nov-16 Boosting A Brief Introduction to Boosting HW 7
10 Wed 23-Nov-16 Active Learning Improving Generalization with Active Learning
10 Fri 25-Nov-16 THANKSGIVING BREAK Classifiers and the illusion of progress HW 9: Final Assignment   10
11 Mon 28-Nov-16 Course wrap-up/preparation for final Reinforcement Learning, An Introduction: Chapter 3 HW 8
11 Wed 30-Nov-16 No class, use this time to do HW 9 Reinforcement Learning, An Introduction: Chapter 6
11 Fri 2-Dec-16 Recitation (last chance for help on HW 9)        
12 Mon 5-Dec-16 HW 9
12 Fri 9-Dec-16 Final Exam 9am - 11am.  NU Final exam schedule Final Exam Final 10
TOTAL 110