Pre-Approved Course List

Updated 9/22/2021

Use ExploreCourses to review course descriptions and find out when they will be offered. If there is a course you would like to take that is not on this list, please submit a Request for Approval of Advanced Level Coursework form prior to the start of the course.

Advanced Courses

APPPHYS 293: Theoretical Neuroscience (Crosslisted as PSYCH 242)

BIO 204: Neuroplasticity: From Synapses to Behavior

BIO 214: Advanced Cell Biology (Crosslisted as BIOC 224, MCP 221)

BIO 222: Exploring Neural Circuits

BIO 224 : Advanced Cell Biology (Crosslisted as BIO 214, MCP 221)

BIO 230:  Molecular and Cellular Immunology

BIO 249: The Neurobiology of Sleep  (Crosslisted as BIO 149, HUMBIO 161)

BIO 268:  Statistical and Machine Learning Methods for Genomics (Crosslisted as BIOMEDIN 245, CS 373, GENE 245, STATS 345)

BIO 276: The Developmental Basis of Animal Body Plan Evolution (Crosslisted as BIO 176)

BIO 283: Theoretical Population Genetics (Crosslisted as BIO 183)

BIOC 224: Advanced Cell Biology (Crosslisted as BIO 214, MCP 221)

BIOE 291: Principles and Practice of Optogenetics for Optical Control of Biological Tissues

BIOE 300B: Quantitative Physiology

BIOE 355: Advanced Biochemical Engineering (Crosslisted as CHEMENG 355)

BIOE 227: Functional MRI Methods (Crosslisted as BIOPHYS 227, RAD 227)

BIOMEDIN 251: Outcomes Analysis (HRP 252, MED 252)

BIOMEDIN 260: Computational Methods for Biomedical Image Analysis and Interpretation (Crosslisted as CS 235, RAD 260)

CME 263:  Introduction to Linear Dynamical Systems (Crosslisted as EE 263)

CME 323: Distributed Algorithms and Optimization

COMPMED 207: Comparative Brain Evolution

CS 205L: Continuous Mathematical Methods with an Emphasis on Machine Learning

CS 228: Probabilistic Graphical Models: Principles and Techniques

CS 229: Applied Machine Learning (Crosslisted as STATS 229)

CS 230: Deep Learning

CS 231N: Convolutional Neural Networks for Visual Recognition

CS236: Deep Generative Models

CS 236G: Generative Adversarial Networks

CS 348E: Character Animation: Modeling, Simulation, and Control of Human Motion

CS 273B : Deep Learning in Genomics and Biomedicine (Crosslisted as BIODS 237, CS 273B, GENE 236)

CS 274: Representations and Algorithms for Computational Molecular Biology (Crosslisted as BIOE 214, BIOMEDIN 214, GENE 214)

CS 329D: Machine Learning Under Distributional Shifts

CS 330: Deep Multi-Task and Meta Reinforcement Learning

CS 375: Large-Scale Neural Network Modeling for Neuroscience (Crosslisted as PSYCH 249)

CS 379C: Computational Models of the Neocortex

CS 422: Interactive and Embodied Learning

DBIO 210: Developmental Biology

EDUC 377G: Problem Solving for Social Change (Crosslisted as GSBGEN 367)

EDUC 464: Measuring Learning in the Brain (Crosslisted as NEPR 464)

EE 261: The Fourier Transform and Its Applications

EE 263 : Introduction to Linear Dynamical Systems (Crosslisted as CME 263)

EE 278 : Introduction to Statistical Signal Processing

EE 364A: Convex Optimization I (Crosslisted as CME 364A, CS 334A)

EE 364B: Convex Optimization II (Crosslisted as CME 364B)

EE 372: Data Science for High Throughput Sequencing

EE 376A: Information Theory

EPI 237: Practical Approaches to Global Health Research (INTLPOL 290, MED 226)

GENE 205: Advanced Genetics

GENE 221: Current Issues in Aging

GENE 229: How We Age

IMMUNOL 206: Introduction to Applied Computational Tools in Immunology

IMMUNOL 286: Neuroimmunity

MCP 222: Imaging: Biological Light Microscopy

MCP 256: How Cells Work: Energetics, Compartments, and Coupling in Cell Biology

ME 300A: Linear Algebra with Application to Engineering Computations (Crosslisted as CME 200)

NBIO 201: Social and Ethical Issues in the Neurosciences

NBIO218: Neural Basis of Behavior

NBIO 224: Glia and Neuroimmunology 

NBIO 227: Understanding Techniques in Neuroscience

NBIO 228: Mathematical Tools for Neuroscience 

NBIO 254: Molecular and Cellular Neurobiology (Crosslisted as BIO 254)

NBIO 258: Information and Signaling Mechanisms in Neurons and Circuits

NENS 267: Molecular Mechanisms of Neurodegenerative Disease (Crosslisted as BIO 267, GENE 267)

NSUR 262: 2 Photon Imaging of Neural Circuits

NSUR 287: Brain Machine Interfaces: Science, Technology, and Application (Crosslisted as PSYCH 287)

OTOHNS 206: Augmenting Human Senses: Enhancing Perception via Technology and Bioscience

PHIL 360 : Grad Seminar: Philosophy of Neuroscience

PHIL 368A: Explanation in Neuroscience

PHYSICS 212:  Statistical Mechanics

PSYC 250: Methodology of Research in Behavioral Sciences

PSYCH 202: Cognitive Neuroscience

PSYCH 204A: Human Neuroimaging Methods

PSYCH 204B: Human Neuroimaging Methods

PSYCH 206: Cortical Plasticity: Perception and Memory

PSYCH 209:  Neural Network Models of Cognition: Principles and Applications

PSYCH 226:  Models and Mechanisms of Memory

PSYCH 232: Brain and Decision Making

PSYCH 234: Special Topics in Depression

PSYCH 240A: Curiosity in Artificial Intelligence (EDUC 234)

PSYCH 250: High-level Vision: From Neurons to Deep Neural Networks (Crosslisted as CS 431)

PSYCH 254: Lab in Experimental Methods

PSYCH 268: Emotion Regulation

PSYCH 287: Brain Machine Interfaces: Science, Technology, and Application (Crosslisted as NSUR 287)

SOMGEN 223: Introduction to R for Data Analysis

STEMREM 201A: Stem Cells and Human Development: From Embryo to Cell Lineage Determination

STEMREM 202: Stem Cells and Human Development Laboratory


Statistics Courses

BIO 141: Biostatistics

COMPMED 211: Robust, reproducible, real-world experimental design and analysis for life and biomedical scientists

CS109: Introduction to Probability for Computer Scientists

MS&E 226: Fundamentals of Data Science: Prediction, Inference, Causality

STATS 116: Theory of Probability

STATS 141:  Biostatistics

STATS 191: Introduction to Applied Statistics

STATS 200: Introduction to Statistical Inference

STATS 202: Data Mining and Analysis

STATS 207: Introduction to Time Series Analysis

STATS 216 : Introduction to Statistical Learning

STATS 215: Statistical Models in Biology

STATS 217: Introduction to Stochastic Processes I

STATS 220: Machine Learning Methods for Neural Data Analysis

STATS 231: Statistical Learning Theory

STATS 305A: Applied Statistics I

STATS 320: Machine Learning Methods for Neural Data Analysis

STATS 325 : Multivariate Analysis and Random Matrices in Statistics

STATS 366: Modern Statistics for Modern Biology (Crosslisted as BIOS 221)

STATS 369: Methods from Statistical Physics