Syllabus Overview:

Week 1: introduction & decision trees

3/28
 Introduction
 Reading: Murphy 1.1  1.3

3/30
 Decision trees
 Slides
 Lecture Notes
 Reading: Murphy 16.2.1  16.2.4
 Read Murphy Chapter 2 for probability background (if needed)
 Optional reading: Mitchell: Chapter 3
 Optional reading: Friedman: 9.2

4/1
 Decision trees
 Slides
 Lecture Notes
 Reading: [same as 3/30]

Week 2: Decision trees & point estimation

4/4 (Monday)
 Decision trees
 Slides
 Lecture Notes
 Reading: [same as 3/30]
 Homework 1 out.

4/6
 Point estimation
 Lecture Notes
 Reading: probability review (as needed): Murphy 2.1, 2.2, 2.5
 Reading: generative models: Murphy 3.1, 3.2, 3.3
 Reading: Bayesian statistics: Murphy 5.1, 5.2
 Reading: Gaussians (we will probably only get this far on Fri): Murphy 4.1
 Optional reading: Mitchell 6.1  6.6
4/7
 Recitation: Python Review

4/8
 Point estimation
 Lecture Notes
 Reading: [same as 4/6]

Week 3: Linear regression

4/11
 Linear regression
 Lecture Notes
 Reading: Murphy 7.1, 7.2, 7.3, 7.5.1, 7.5.4
 Optional reading: Friedman 3.1, 3.2, 3.4.1, 3.4.2
 Optional reading: Bishop 3.1.1, 3.1.2, 3.1.3, 3.1.4

4/13
 Linear regression
 Lecture Notes
 Slides
 Reading: [same as 4/11]
4/14
 Recitation: Linear Algebra Review

4/15
 Naive Bayes
 Lecture Notes
 Slides
 Reading: Murphy 3.5
 Optional reading: Mitchell 6.9

Week 4: Naive Bayes

4/18 (Monday)
 Homework 2 out. [pdf] [tex(zip)][data (mnist.zip)]
 Naive Bayes
 Lecture Notes
 Slides
 Reading: [same as 4/15]

4/20
 Logistic Regression
 Lecture Notes
 Slides
 Reading: Murphy 8.1, 8.2, 8.3
 Optional reading: Friedman 4.4
 Programming section out for homework2

4/22 (Friday)
 Homework 1 due.
 Logistic Regression
 Lecture Notes
 Reading: Murphy 8.6

Week 5: Neural networks

4/25
 Logistic Regression and Neural Networks
 Lecture Notes
 Reading: Murphy 16.5
 Optional reading: Bishop 5.1, 5.2, 5.3
 Optional reading: Friedman 11.3, 11.4
 Optional reading: Mitchell 4.1, 4.2, 4.3, 4.5, 4.6

4/27
 Neural Networks
 Lecture Notes
 Reading: [same as 4/25]

4/29
 Neural Networks
 Lecture Notes
 Reading: [same as 4/25]

Week 6: Support vector machines (SVMs)

5/2 (Monday)
 Support vector machines (SVMs)
 Homework 3 out.
 Midterm Study Topics
 Lecture Notes
 Reading: Murphy 8.5.4
 Additional reading: Andrew Ng's lecture notes 16 (highly recommended, though notation is a little different from mine)
 Optional reading: Bishop 7.1
 Optional reading: Friedman 12.1, 12.2

5/4 (Wednesday)
 Lecture Notes
 Reading: Murphy 14.2, 14.5.2, 14.5.3, 14.5.4
 Additional reading: Andrew Ng's lecture notes 7
 Optional reading: Friedman 12.3

5/5 (Thursday)
 Midterm review.

5/6 (Friday)
 Midterm.

Week 7: Model ensembles

5/9 (Monday)
 Homework 2 due.
 SVM Lecture Notes
 Ensembles Lecture Notes
 Ensembles Slides
 Reading: Murphy 16.2.5, 16.4
 Additional reading: John Duchi's lecture notes
 Optional reading: Bishop 14.1, 14.3
 Optional reading: Friedman 10.1, 10.2, 10.3, 10.4

5/11 (Wednesday)
 Lecture Notes
 Slides
 Reading: [same as 5/9]

5/13 (Friday)
 Lecture Notes
 Slides
 Reading: Murphy 6.3, 6.4, 6.5
 Optional reading: Friedman 7.1  7.10
 Optional reading: Mitchell Chap 7

Week 8: Learning theory, clustering

5/16 (Monday)
 Lecture Notes
 Slides
 Additional reading: No Free Lunches for Anyone
 Reading: [same as 5/13]

5/18 (Wednesday)
 Lecture Notes
 Slides
 Lecture: watch online and come to class with questions! See Piazza
 Reading: Murphy 11.1, 11.2, 11.3, 11.4.1, 11.4.2
 Optional reading: Friedman 13.2.1  13.2.3
 Optional reading: Bishop Chap 9

5/19 (Thursday)
 Section: review midterm questions and differentiation

5/20 (Friday)
 Lecture Notes
 Slides
 Lecture: watch online and come to class with questions! See Piazza
 Reading: [same as 5/18]

Week 9: Dimensionality reduction

5/23 (Monday)
 Homework 3 due.
 Homework 4 out. [pdf] [tex][data (zip)]
 Lecture Notes
 Reading: [same as 5/18]

5/25 (Wednesday)
 Lecture Notes
 Reading: Murphy 1.3.2, 12.2

5/27 (Friday)
 Lecture Notes
 Reading: [same as 5/25]
Week 10: Review & conclusions

Week 11: Finals week

6/6 (Monday)
 Homework 4 due.

6/8 (Wednesday)
 Final exam: 8:30 am
Text Books:
 Machine Learning: a Probabilistic Perspective, Kevin Murphy, MIT Press, 2013.
 Optional: Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2007.
 Optional: Machine Learning, Tom Mitchell, McGrawHill, 1997.
 Optional: The Elements of Statistical Learning, Friedman, Tibshirani, Hastie, Springer, 2001.
Homeworks:
We will have 4 homework assignments, which will be listed below as they are assigned. The assignments will be given out roughly in weeks 2, 4, 6, and 8, and you will have two weeks to complete each one.
 Assignment 1: Decision Trees, Point Estimation (Due: Friday 4/22)
 Assignment 2: Supervised Learning I: Regression, Naive Bayes, Neural nets (Due: Monday 5/9)
 Assignment 3: Supervised Learning II: SVMs and Ensembles (Due: Monday 5/23)
 Assignment 4: Unsupervised learning (Due: Moday 6/6)
Submission instructions will be posted here once the first homework is assigned.
Please be careful to not overwrite an in time assignment with a late assignment when uploading near the deadline.
Each student has three penaltyfree late day for the whole quarter, other than that any late submission will be penalized for each day it is late.
Exam:
There will be final and midterm (6th week) exams for this course (Time and location TBA). The exams are open note, you are welcome to bring the book, the lecture slides, and any handwritten notes you have.
Grading:
The final grade will consist of homeworks (65%), a midterm exam (10%), a cumulative final exam (20%), and inclass participation (5%).
Course Administration and Policies
 Assignments will be done individually unless otherwise specified. You may discuss the subject matter with other students in the class, but all final answers must be your own work. You are expected to maintain the utmost level of academic integrity in the course.
 As we sometimes reuse problem set questions from previous years, please do not to copy, refer to, or look at any solution keys while preparing your answers. Doing so will be regarded as cheating. We expect you to want to learn and not google for answers.
 Each student has three penaltyfree late day for the whole quarter. Beyond that, late submissions are penalized (10% of the maximum grade per day)
 Comments can be sent to the instructor or TA using this anonymous feedback form . We take all feedback very seriously and will do whatever we can to address any concerns.