DATASCI 315 Fall 2024

Statistics and Artificial Intelligence

Overview

Statistical concepts are increasingly integrated into artificial intelligence applications, which often draw on a large amount of data received, transmitted, and generated by computers or networks of computers. This course introduces students to statistics and machine learning techniques such as deep neural networks, with applications to text and image data.

At the end of this course, students will be familiar with the deep learning paradigm, and will be able to analyze data using different classes of deep learning models. The course gives an introduction to the basics of deep neural networks, and their applications to various AI tasks.

Syllabus

For course policies, course requirements, and grading policies, please see the syllabus [link].

Piazza

Students should sign up Piazza [link] to join course discussions.

All communications with the teaching team (the instructor and the GSIs) should be conducted over Piazza; please do not email. If you'd like to reach the instructor or the GSIs for private questions, please post a private note on Piazza that is only visible to the instructor and the GSIs. See here for detailed instructions. The GSIs and the instructor will be monitoring Piazza, endorsing correct student answers, and answering questions that remain after a discussion.

As a bonus, up to 3 percentage points will be added to your final course grade based on piazza participation. You will get (3x/100) bonus percentage points if the number of your total Piazza contributions is (x * 100)% of the maximum number of contributions among all students. The number of Piazza contributions will be determined by Piazza class statistics.

Teaching Team, Office Hours, and Labs

  • Instructor: Yixin Wang — weekly Office Hours.
  • GSIs:
    • Sahana Rayan — Wednesday 10–11:30am; Thursday 10–11:30am (in person).
    • Jacob Trauger — Monday 12–3pm (in person).
    • Andrej Leban — Wednesday 3–4pm (in person); Tuesday 10–11am and Friday 12–1pm online.
  • Locations: In person: Angell Hall G219. Virtual: Zoom (passcode: 115588).

Please refer to the course calendar for office hour details.

Course Calendar

  • Lecture: Tue/Thur 8:30am-9:50am
  • Location: 1210 CHEM
  • Google Calendar: The Google Calendar below ideally contains all events and deadlines for student's convenience. Please feel free to add this calendar to your Google Calendar by clicking on the plus (+) button on the bottom right corner of the calendar below. Any adhoc changes to the schedule will be visible on the calendar first.

Lecture Schedule

The Schedule is subject to change.

DLPy = Deep Learning with Python (2nd edition) by Chollet
DL = Deep Learning by Goodfellow, Bengio and Courville [link]
NNDL = Neural Networks and Deep Learning by Nielsen [link]
D2L = Dive into Deep Learning by Zhang, Lipton, Li and Smola [link]
Date Topic Readings

Lecture 1

08/27

Introduction

DLPy What is deep learning?, Chap. 1
DL Introduction, Chap. 1
D2L Introduction, Chap. 1

Lecture 2

08/29

Vectorization and Linear Algebra Bootcamp I

D2L Geometry and Linear Algebraic Operations, Sec. 22.1.1-9

Lecture 3

09/03

Vectorization and Linear Algebra Bootcamp II

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Lecture 4

09/05

Regression as Deep Learning I

D2L Linear Regression, Sec. 3.1.1-4
D2L Softmax Regression, Sec. 4.1.1
D2L Loss Function, Sec. 4.1.2

Lecture 5

09/10

Regression as Deep Learning II

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Lecture 6

09/12

Regression as Deep Learning III

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Lecture 7

09/17

Regression as Deep Learning IV

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Lecture 8

09/19

Regression as Deep Learning V

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Lecture 9

09/24

Regression as Deep Learning VI

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Lecture 10

09/26

First Steps with TensorFlow

DLPy, Sec. 2.4.4
DLPy, Sec. 3.1-4
DLPy, Sec. 3.5.1-4

Lecture 11

10/01

Shallow Neural Networks with Keras

DLPy, Sec. 2.1, Sec. 4.1-3

Lecture 12

10/03

Opening the Black Box of Keras

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Lecture 13

10/08

Getting started with NNs: Classification and Regression I

DLPy, Sec. 2.1, Sec. 4.1-3

Lecture 14

10/10

Getting started with NNs: Classification and Regression II

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Fall break

10/15

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Lecture 15

10/17

Fundamentals of ML I

DLPy, Sec. 5.1-3, 5.4.4, 6.3

Lecture 16

10/22

Fundamentals of ML II

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Lecture 17

10/24

Midterm Exam

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Lecture 18

10/29

Convolutional Neural Networks I

D2L, Sec. 7.1-6
DLPy, Sec. 7.2

Lecture 19

10/31

Convolutional Neural Networks II

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Lecture 20

11/05

Convolutional Neural Networks III

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Lecture 21

11/07

Convolutional Neural Networks IV

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Lecture 22

11/12

Convolutional Neural Networks V

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Lecture 23

11/14

Deep Learning for Sequence Data I

DLPy, Sec. 10.2-4

Lecture 24

11/19

Deep Learning for Sequence Data II

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Lecture 25

11/21

Deep Learning for Sequence Data III

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Lecture 26

11/26

Deep Learning for Sequence Data IV

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Thanksgiving break

11/28

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Lecture 27

12/03

Deep Generative Modeling

D2L, Chap. 18

Lecture 28

12/05

Summary (and wiggle room)

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Acknowledgements

The course materials are adapted from the related courses offered by Alexander Amini, Alfredo Canziani, Justin Johnson, Andrew Ng, Bhiksha Raj, Grant Sanderson, Rita Singh, Ava Soleimany, and Ambuj Tewari.