Course Information

Course: TuFri 9:30–11am
Professor: Sara Mathieson
Office: KINSC L302
Office hours: Mon 9:45-11am, Tues 4:30-6pm
TAs: Jason Ngo, Fiona Xu
TA hours: Sun 8:30-10pm (Fiona), Thurs 8-9:30pm (Jason)

The prerequisites for this course are Data Structures, Discrete Mathematics, and Linear Algebra.

Machine Learning as a field has grown considerably over the past few decades. In this course, we will explore both classical and modern approaches, with an emphasis on theoretical understanding. There will be a significant math component (statistics and probability in particular), as well as a substantial implementation component (as opposed to using high-level libraries). However, during the last part of the course we will use a few modern libraries such as TensorFlow and Keras. By the end of this course, you should be able to form a hypothesis about a dataset of interest, use a variety of methods and approaches to test your hypothesis, and be able to interpret the results to form a meaningful conclusion. We will focus on real-world, publicly available datasets, not generating new data.

The language for this course is Python 3.

Textbook:

You do not need to purchase a textbook for this course. We will draw from two online textbooks, as well as supplemental online readings and research papers.

• A Course in Machine Learning by Hal Duame III ("Duame" in schedule below)

• An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani ("ISL" in schedule below)

Format:

See the Computer Science department teaching plans for Fall 2020.

• CS360 is online only.
• Synchronous participation during class meetings and lab is required. Up to 3 class meetings may be missed without penalty to the participation grade. Please email me if you will be missing class or lab.
• There will be one short video posted Sunday night (to be watched before class on Tuesday).

See the Schedule for each week's reading assignment. The schedule is tentative and subject to change throughout the semester.

Schedule (Tentative)

WEEK	DAY	ANNOUNCEMENTS	TOPIC & READING	LABS
1	Sep 08		Introduction to Machine Learning Machine learning terminology Notation K-nearest neighbors Featurization Reading: Duame 1.1, 1.2 Duame 3.1-3.3 Machine Learning by Tom Dietterich in Nature Encyclopedia of Cognitive Science (skim Sections 4-7) (optional) ISL: Chapter 1 (optional) ISL: Sections 2.1, 2.2 (focus on 2.1 and pg. 39-42) (optional) The Discipline of Machine Learning by Tom Mitchell	Tues: Slides 1 Fri: Slides 2 Handout 1 Lab 1: K-nearest neighbors
	Sep 11
2	Sep 15		Decision Trees ML workflow Entropy Decision trees Reading: Duame 1.3-1.6 Duame Chap 2 (optional) ISL: Section 8.1 (optional) Induction of Decision Trees by J.R. Quinlan (1986) (optional) Machine-learning-assisted materials discovery using failed experiments by Raccuglia et al (2016) (optional) Decision Trees: An Overview and Their Use in Medicine by Podgorelec et al (2002) (optional) Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications by Galindo and Tamayo (2000)	Tues: Slides 3 Handout 2 Fri: Slides 4 Handout 3 Lab 2: Decision trees
	Sep 18	Last day to drop (Sep 20)
3	Sep 22		Linear Regression Loss functions Bias-Variance tradeoff Linear regression Polynomial regression Reading: ISL: Chap 3, pg 59-63 Duame 7.6 (optional) ISL: Sections 3.1-3.5 (optional) Simple linear regression by James Kirchner (2001)	Tues: Slides 5 Fri: Slides 6 Handout 4 Lab 3: Polynomial Regression
	Sep 25
4	Sep 29		Probabilistic Models 1 Introduction to probability Naive Bayes Reading: Duame 9.3 Generative And Discriminative Classifiers: Naive Bayes And Logistic Regression (Section 2) by Tom Mitchell (optional) ISL: Chapter 4 (optional) On Discriminative vs. Generative Classifiers by Andrew Ng and Michael I. Jordan (2001) (optional) Estimating Probabilities by Tom Mitchell (optional) The Naive Bayes Model, Maximum-Likelihood Estimation, and the EM Algorithm by Michael Collins	Tues: Slides 7 Handout 5 Fri: Slides 8 Handout 6 Study Guide 1 Lab 4: Naive Bayes
	Oct 02
5	Oct 06		Probabilistic Models 2 Likelihood functions Logistic regression Reading: Stanford Course Notes pg 14-17 (optional) Generative And Discriminative Classifiers: Naive Bayes And Logistic Regression (Section 3) by Tom Mitchell (optional) Softmax Regression (multi-class logistic regression) from the Stanford UFLDL Tutorial	Tues: Slides 9 Handout 7 Fri: Slides 10 Handout 8 Midterm 1
	Oct 09	Last day to pass/fail (Oct 11)
6	Oct 13		Evaluation Metrics Confusion matrices Precision and recall ROC curves Relationship to probabilistic models Cross-Validation Reading: ISL: Section 4.4.3 8.3 and 8.4 of Evaluation in information retrieval by Manning et al (2009)	Tues: Slides 11 Handout 9 Fri: Slides 12 Lab 5: Logistic Regression
	Oct 16
7	Oct 20		Ensemble Methods Bagging Random forests Boosting Reading: Duame Chap 13 Ensemble Methods in Machine Learning by Tom Dietterich (optional) ISL: Section 8.2 (optional) Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers by Wyner et al (2017)	Tues: Slides 13 Handout 10 Handout 11 Fri: Slides 14 Handout 12 Lab 6: Ensemble methods
	Oct 23
8	Oct 27		Support Vector Machines Perceptron Support vector machines Lagrange multipliers SVM optimization problems Kernels Reading: Duame Chap 4 A User's Guide to Support Vector Machines, by Asa Ben-Hur and Jason Weston (optional) ISL: Sections 9.1-9.3 (optional) Perceptron, notes by Andrew Ng (optional) Support Vector Machines, notes by Andrew Ng	Tues: Slides 15 Optional: Perceptron SVM Handout 13 Handout 14 SVM problem set Lab 7: sklearn
	Oct 30
9	Nov 03		Topics in Deep Learning 1 Introduction to neural networks Fully connected architectures Reading: From Stanford CNN course: Neural Networks 1 Neural Networks 2 Neural Networks 3 (optional) Backpropagation	Optional: Bias in ML
	Nov 06
10	Nov 10		Topics in Deep Learning 2 Convolutional neural networks (CNNs) Midterm 2 review Reading: From Stanford CNN course: Convolutional Neural Networks Understanding and Visualizing CNNs	Tues: Slides 18 Fri: Slides 19 Handout 15 Lab 8: Neural Networks
	Nov 13
11	Nov 17		Unsupervised Learning 1 K-means clustering Reading: ISL: Sections 10.1-10.3	Tues: Slides 20 Handout 16 Fri: Slides 21 Handout 17 Handout 17 solution Lab 8 (cont)
	Nov 20
	Nov 24	Thanksgiving Break
	Nov 27
12	Dec 01		Unsupervised Learning 2 Gaussian mixture models Hierarchical clustering Dimensionality Reduction Principal components analysis	Tues: Slides 22 Study Guide 2 Fri: Slides 23 Handout 18 Tues: Slides 24 Handout 19 Handout 20 Handout 20 solution Midterm 2 Project
	Dec 04
13	Dec 08		Special Topic: Machine Learning and Ethics Deep learning in biology Learning from biased datasets Reading: How big data is unfair by Moritz Hardt (2014) Semantics derived automatically from language corpora contain human-like biases by Caliskan et al (2017) Think Piece: On the Cruelty of Really Writing a History of Machine Learning by Aaron Plasek (2016) Certifying and Removing Disparate Impact by Feldman et al (2015) Big Data’s Disparate Impact by Barocas and Selbst Deep Learning: A Critical Appraisal by Gary Marcus (2018)
	Dec 11
14	Dec 15
	Dec 18

Grading Policies

Grades will be weighted as follows (UPDATED BELOW):

40%	Lab assignments
25%	Midterm I
25%	Midterm II *OR* Final Project (including presentation)
10%	Participation

Quizzes and Exams

In lieu of reading quizzes this semester, we will have short excercises during class (to work on and discuss, not turn in). Be ready to work on these exercises by completing the weekly reading before class on Fridays.

There will be two midterms (with limited time, but you will have several days to choose a window). In lieu of a final exam, there will be a final project and associated presentation. You must pass at least one exam to pass the course overall.

Labs

Our labs are on Thursdays. Lab assignments will generally be released Tuesday night and due the following Tuesday at midnight (with a grace period before collection due to timezone differences). You are expected to read/begin the lab before your lab section on Thursday. Lab attendance is required, and missing labs will quickly affect your participation grade. There will often be pair-programming warm-up exercises as part of the lab, and lab in general is a time to build community around the course and the material. Note that Wednesday is my research day and I will be off campus and unable to answer lab questions. Make use of office hours (both mine and the TAs) and Piazza.

Weekly Lab Sessions
Lab A 9:30—10:30am Thursdays	Mathieson	Zoom
Lab B 11am—12pm Thursdays	Mathieson	Zoom

Handing in labs: Lab assignments are submitted electronically and managed using git. You may submit your assignment multiple times, but each submission overwrites the previous one and only the final submission will be graded. Some of the programming/lab assignments may be in pairs. There may also be some written assignments that will have specific instructions for handing in.

Late Policy: Each individual will be given 2 late days for the semester. A late day is a 24 hour extension from the original deadline + grace period. You can use one day on two assignments or both days on one assignment. This will encompass any reason - illness, interviews, many midterms in the same week, etc. Past these days, late assignments will not be accepted. You should budget your days to account for future illnesses or assignment deadlines for other courses. Even if you do not fully complete a lab assignment you should submit what you have done to receive partial credit. Late days count against both partners in a group lab.

For extensions beyond these 2 late days (in the case of an emergency or ongoing personal issue), please contact your Class Dean. If your Class Dean notifies me of the issues, then we can arrange an accommodation.

Academic Integrity

From the faculty:

In a community that thrives on relationships between students and faculty that are based on trust and respect, it is crucial that students understand a professor's expectations and what it means to do academic work with integrity. Plagiarism and cheating, even if unintentional, undermine the values of the Honor Code and the ability of all students to benefit from the academic freedom and relationships of trust the Code facilitates. Plagiarism is using someone else's work or ideas and presenting them as your own without attribution. Plagiarism can also occur in more subtle forms, such as inadequate paraphrasing, failure to cite another person's idea even if not directly quoted, failure to attribute the synthesis of various sources in a review article to that author, or accidental incorporation of another's words into your own paper as a result of careless note-taking. Cheating is another form of academic dishonesty, and it includes not only copying, but also inappropriate collaboration, exceeding the time allowed, and discussion of the form, content, or degree of difficulty of an exam. Please be conscientious about your work, and check with me if anything is unclear.

Please also note the CS Department Collaboration Policy.

More details for this course:

Under no circumstances may you hand in work done with (or by) someone else under your own name. Your code should never be shared with anyone; you may not examine or use code belonging to someone else, nor may you let anyone else look at or make a copy of your code. This includes, but is not limited to, obtaining solutions from students who previously took the course or code that can be found online. You may not share solutions after the due date of the assignment.

Discussing ideas and approaches to problems with others on a general level is fine (in fact, we encourage you to discuss general strategies with each other), but you should never read anyone else's code or let anyone else read your code. All code you submit must be your own with the following permissible exceptions: code distributed in class, code found in the course text book, and code worked on with an assigned partner. In these cases, you should always include detailed comments that indicates on which parts of the assignment you received help, and what your sources were.

Piazza

This semester we’ll be using Piazza, an online Q&A forum for class discussion, help with labs, clarifications, and announcements. You should have received an email invitation to join CMSC H360 on Piazza. If you didn't, please let me know.

Piazza is meant for questions outside of regular meeting times such as office hours, class, and lab. Please do not hesitate to ask and answer questions on Piazza, but keep in mind the following guidelines:

1. Piazza should be used for ALL content and logistics questions outside of class, lab, and office hours. Please do not email me your code or extended questions about the assignments.
2. If there is a personal issue that relates only to you, please email me.
3. We encourage non-anonymous posts, but you may post anonymously (to your classmates, not the instructors).
4. Do not post long blocks of code on Piazza - if you can distill the problem to 1-2 lines of code and an error message, that’s fine, but try to avoid giving out key components of your work.
5. By the same token, when answering a question, try to give some guiding help but do not post code fixes or explicit solutions to the problem.
6. Posting on Piazza counts toward your participation grade, both asking and answering!

Academic Accommodations

For details about the accommodations process, visit the Access and Disability Services website.

Haverford College is committed to providing equal access to students with a disability. If you have (or think you have) a learning difference or disability – including mental health, medical, or physical impairment - please contact the Office of Access and Disability Services (ADS) at hc-ads@haverford.edu. The Coordinator will confidentially discuss the process to establish reasonable accommodations.

Students who have already been approved to receive academic accommodations and want to use their accommodations in this course should share their verification letter with me and also make arrangements to meet with me as soon as possible to discuss their specific accommodations. Please note that accommodations are not retroactive and require advance notice to implement.

It is a state law in Pennsylvania that individuals must be given advance notice if they are to be recorded. Therefore, any student who has a disability-related need to audio record this class must first be approved for this accommodation from the Coordinator of Access and Disability Services and then must speak with me. Other class members will need to be aware that this class may be recorded.

Useful Links

Machine Learning notation (we will follow this loosely)
Official Python style guide
Python 3 Documentation
Atom editor