DATASCI 315 Fall 2026

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 through Canvas 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 and Office Hours

  • Instructor: Yixin Wang — weekly Office Hours.
  • GSIs: Xiaoyu Qiu, Dhruba Nandi, and Siyuan Tang — office hours TBD.

Please refer to the course calendar for details.

Course Calendar

  • Lecture: Tue/Thur 11:30am-12:50pm
  • Location: 2260 USB
  • Lab 002: Thur 2:30pm-4:00pm, 296 WEISER
  • Lab 003: Thur 4:00pm-5:30pm, 1372 EH
  • Lab 004: Thur 5:30pm-7:00pm, B760 EH
  • 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 Assignments

Lecture 1

09/01

Introduction

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

HW01

Lecture 2

09/03

Vectorization and Linear Algebra Bootcamp I

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

Lab01

Lecture 3

09/08

Vectorization and Linear Algebra Bootcamp II

''

HW02

Lecture 4

09/10

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

Lab02

Lecture 5

09/15

Regression as Deep Learning II

''

Quiz 1
HW03

Lecture 6

09/17

Regression as Deep Learning III

''

Lab03

Lecture 7

09/22

Regression as Deep Learning IV

''

HW04

Lecture 8

09/24

Regression as Deep Learning V

''

Lab04

Lecture 9

09/29

Regression as Deep Learning VI

''

Quiz 2
HW05

Lecture 10

10/01

First Steps with TensorFlow

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

Lab05

Lecture 11

10/06

Shallow Neural Networks with Keras

DLPy, Sec. 2.1, Sec. 4.1-3

HW06

Lecture 12

10/08

Opening the Black Box of Keras

''

Lab06

Lecture 13

10/13

Getting started with NNs: Classification and Regression I

DLPy, Sec. 2.1, Sec. 4.1-3

HW07

Lecture 14

10/15

Midterm Exam

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Lab07

Fall break

10/20

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Lecture 15

10/22

Getting started with NNs: Classification and Regression II

''

No lab this week

Lecture 16

10/27

Fundamentals of ML

DLPy, Sec. 5.1-3, 5.4.4, 6.3

HW08

Lecture 17

10/29

Convolutional Neural Networks I

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

Lab08

Lecture 18

11/03

Convolutional Neural Networks II

''

Quiz 3
HW09

Lecture 19

11/05

Convolutional Neural Networks III

''

Lab09

Lecture 20

11/10

Convolutional Neural Networks IV

''

HW10

Lecture 21

11/12

Convolutional Neural Networks V

''

Lab10

Lecture 22

11/17

Deep Learning for Sequence Data I

DLPy, Sec. 10.2-4

Quiz 4
HW11

Lecture 23

11/19

Deep Learning for Sequence Data II

''

Lab11

Lecture 24

11/24

Deep Learning for Sequence Data III

''

HW12

Thanksgiving break

11/26

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No lab this week

Lecture 25

12/01

Deep Learning for Sequence Data IV

''

Quiz 5
HW13

Lecture 26

12/03

Generative AI

D2L, Chap. 18

Lab12

Lecture 27

12/08

Guest lecture

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Lecture 28

12/10

Guest lecture

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No lab this week

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.