# B.S. with a Specialization in Machine Learning and Neural Computation

## Major Code: CG35

A major may elect to receive a B.S. in Cognitive Science with specialization in Machine Learning and Neural Computation. This area of specialization is intended for majors interested in computational and mathematical approaches to modeling cognition or building cognitive systems, theoretical neuroscience, as well as software engineering and data science. Allowed electives include advanced courses in neural networks, artificial intelligence, and computer science.

## Major Requirements

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### Lower Division Requirements

(11 courses, 44 units or 10 courses, 40 units)

#### Math

- MATH 10A, 10B, 10C, 18
- OR MATH 20A, 20B, 18 *

* Machine Learning students are strongly advised to take MATH 20-A-B-C-E and MATH 18 and 180A, as they are pre-requisites for COGS 118-A-B-C-D, of which 2 are required for the Machine Learning Specialization.

#### Cognitive Science

**Introduction:**COGS 1**Design:**COGS 10 or DSGN 1**Methods:**COGS 13, 14A, 14B**Neuroscience:**COGS 17**Programming:**COGS 18 * or CSE 8A or 11 or MAE 8

* Machine Learning students are strongly advised to take COGS 18, as it is a pre-requisite for Cogs 118A-B-C-D, of which 2 are required for the Machine Learning Specialization.

### Upper Division Requirements

(12 courses, 48 units)

#### Core (6 courses)

**Distributed Cognition:**COGS 100**Fundamental Cognitive Phenomena**(*choose any 2*)**:**COGS 101A, 101B, 101C**Cognitive Neuroscience**(*choose any 2*)**:**COGS 107A, 107B, 107C**Computation:**COGS 108

#### Electives (6 courses)

- A total of 6 electives are required, where at least 3 of the 6 electives must be taken within the Cognitive Science department. At least 4 of the 6 electives must be taken from the approved specialization elective list.
**Students specializing in Machine Learning and Neural Computation must choose 2 from this group of classes for their Specialization Electives: COGS 118A, 118B, 118C, and 118D.**- One course in the Cognitive Science 19X series may be used as an elective to satisfy the requirements for the B.S. degree, but only with the approval of both the instructor who supervised the course and the undergraduate advisor.
- COGS 160 may only be used once for an elective.

Approved Electives (PDF)

Approved Specialization Electives (PDF)

#### Alert

Courses for the major must be taken for a letter grade (with the exception of 195, 198, and 199 which are only offered on a P/NP basis). A minimum grade of C- is required for all courses.## Machine Learning and Neural Computation Faculty

**Virginia de Sa**. Professor, CSB 164, 858-822-5095, vdesa@cogsci.ucsd.edu, website. Research: We use computational modeling, psychophysics studies, and machine learning to learn more about visual and multi-sensory perception.

**Eran Mukamel**. Assistant Professor, emukamel@ucsd.edu, website. Research: Computational analysis of large-scale neural data, electrophysiology of sleep and general anesthesia, computational epigenomics in brain cells.

**Zhuowen Tu**. Associate Professor, SSRB 250, 858-822-0908, ztu@ucsd.edu, website. Research: Computer vision, machine learning, deep learning, neural computation, neuro imaging.

**Bradley Voytek**. Associate Professor, CSB 169, 858-534-0002, bvoytek@ucsd.edu, website. Oscillatory network communication, automated science, data-mining, aging, attention, working memory, cognitive brain-computer interfaces, brain/cognition/society interactions.

**Angela J. Yu**. Associate Professor, CSB 157, 858-822-3317, ajyu@cogsci.ucsd.edu, website. Research: Decision making, attention, active vision, learning, neuromodulation, Bayesian modeling, control theory.

### Faculty with Related Research

**Philip Guo**. Assistant Professor, CSB 129, pg@ucsd.edu, website. Research: Human-computer interaction, design, online learning, computing education, programmer productivity.

**Ayse P. Saygin**. Associate Professor, SSRB 202-20, 858-822-1994, saygin@cogsci.ucsd.edu, website. Research: Cognitive neuroscience, neuropsychology, neuroimaging, visual perception, attention, multisensory integration, biological motion, social neuroscience, language comprehension, human-machine interaction, social robotics.

**Terrence J. Sejnowski**. Adjunct Professor, CNL/Salk, 858-453-4100 Ext. 1611, terry@salk.edu, website. Research: Computational neurobiology; the representation, transformation, and storage of information in the nervous system.

## Recommended Machine Learning and Neural Computation Courses

**COGS 8. Hands-on Computing (4)**

Introductory-level course that will give students insight into the fundamental concepts of algorithmic thinking and design. The course will provide the students with first-person, hands-on experience programming a web crawler and simple physical robots.

**COGS 9. Introduction to Data Science (4)**Concepts of data and its role in science will be introduced, as well as the ideas behind data-mining, text-mining, machine learning, and graph theory, and how scientists and companies are leveraging those methods to uncover new insights into human cognition.

**COGS 18. Introduction to Python (4)**This class will teach fundamental Python programming skills and practices, including the "Zen of Python." Students will focus on scientific computing and learn to write functions and tests, as well as how to debug code, using Jupyter notebook programming environment.

**COGS 108. Data Science in Practice (4)**

Data science is multidisciplinary, covering computer science, statistics, cognitive science and psychology, data visualization, artificial intelligence, and machine learning, among others. This course teaches critical skills needed to pursue a data science career using hands-on programming and experimental challenges. **Prerequisites:** Cognitive Science 18 or MAE 8 or CSE 8A or CSE 11.

**COGS 109. Modeling and Data Analysis (4)**

Exposure to the basic computational methods useful throughout cognitive science. Computing basic statistics, modeling learning individuals, evolving populations, communicating agents, and corpus-based linguistics will be considered. **Prerequisites: ** (COGS18 or CSE11 or CSE8B) and (MATH18 or MATH31AH) and (MATH20E) and (MATH180A) and (COGS108 or COGS109 or COGS118B or CSE150 or CSE151 or CSE158 or ECE174 or ECE175A)

**COGS 118A. Supervised Machine Learning Algorithms (4)**

This course introduces the mathematical formulations and algorithmic implementations of the core supervised machine learning methods. Topics include: regression, nearest neighborhood, decision tree, support vector machine, and ensemble classifiers. **Prerequisites: ** CSE 8B or CSE 11 and Math 18 or Math 31AH and Math 20E and Math 180A or consent of instructor.

**COGS 118B. Intro to Machine Learning II (4)**

This course, with Cognitive Science 118A, forms a rigorous introduction to machine learning. Cognitive Science 118A-B may be taken in either order. Topics in 118B include: maximum likelihood estimation, Bayesian parameter estimation, clustering, principal component analysis, and some application areas. **Prerequisites: ** CSE 8B or CSE 11 and Math 18 or Math 31AH and Math 20E and Math 180A or consent of instructor.

**COGS 118C. Neural Signal Processing (4)**

This course will cover theoretical foundations and practical applications of signal processing to neural data. Topics include EEG/field potential methods (filtering, Fourier (spectral) analysis, coherence) and spike train analysis (reverse correlation, spike sorting, multielectrode recordings). Some applications to neural imaging (optical microscopy, fMRI) data will also be discussed. **Prerequisites: ** Math 18 or Math 31AH, Cognitive Science 14B or Psychology 60, and Cognitive Science 108 or Cognitive Science 109.

**COGS 118D. Mathematical Statistics for Behavioral Data Analysis (4)**

Statistical methods for analyzing behavioral data. A mathematically sophisticated course covering both classical and Bayesian statistical methods for estimation, hypothesis testing, regression, and model comparison. Emphasis on both mathematical understanding of statistical methods as well as common applications. **Prerequisites: ** Math 18 or Math 31AH and Math 180A or consent of instructor.

**COGS 180. Decision Making in the Brain (4)**This course covers recent advances in the understanding of neural mechanisms and computational principles underlying the brain’s ability to make decisions. The role of various factors, as well as their neural encoding, will be considered, e.g., observation noise, reward, risk, internal uncertainty, emotional state, external incentives.

**Prerequisites:**BILD 12 or Cognitive Science 107B or PSYC 106 and Math 31AH or Math 18 and Math 20B and Cognitive Science 108 or Cognitive Science 109 or COGS 18 or CSE 8A or CSE 11.

**COGS 181. Neural Networks and Deep Learning (4)**

This course will cover the basics about neural networks, as well as recent developments in deep learning including deep belief nets, convolutional neural networks, recurrent neural networks, long-short term memory, and reinforcement learning. We will study details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. **Prerequisites: **(COGS18 or CSE11 or CSE8B) and (MATH18 or MATH31AH) and (MATH20E) and (MATH180A) and (COGS118A or COGS118B or CSE150 or CSE151 or CSE158 or ECE174 or ECE175A)

**COGS 185. Advanced Machine Learning Methods (4)**

This course is an advanced seminar and project course that follows the Introduction to Machine Learning courses. Advanced and new machine learning methods will be discussed and used.** Prerequisites:** Cognitive Science 118B or Cognitive Science 118A.

**COGS 188. Artificial Intelligence Algorithms (4)**

This class will cover a broad spectrum of machine learning algorithms. It builds on students' previous exposure to machine learning. It covers new artificial intelligence algorithms ranging from topic models as used in the text data analysis to genetic algorithms.** Prerequisites:** Cognitive Science 109 or Cognitive Science 118A or Cognitive Science 118B.

**COGS 189. Brain Computer Interfaces (4)**

This course will discuss signal processing, pattern recognition algorithms, and human-computer interaction issues in EEG-based brain-computer interfaces. Other types of brain-computer interfaces will also be discussed. **Prerequisites: **Cognitive Science 108 or Cognitive Science 109 or Cognitive Science 118A or Cognitive Science 118B.

## Career and Graduate School Resources

**Possible careers**: software engineer, artificial intelligence engineer, data science engineer, data visualization specialist, statistician, computer science teacher.

- What Do CogSci Students Do After Graduation?
- Preparing for Graduate School
- Make an appointment with a Career Center Advisor to find out more about the options available for someone with your education and experience.