## CS 224D: Deep Learning for Natural Language Processing

Deep learning approaches have obtained very high performance across many different natural language processing tasks. In this class, students will learn to understand, implement, train, debug, visualize and potentially invent their own neural network models for a variety of language understanding tasks. The course provides a deep excursion from early models to cutting-edge research. Applications will range across a broad spectrum: from simple tasks like part of speech tagging, over sentiment analysis to question answering and machine translation. The final project will involve implementing a complex neural network model and applying it to a large scale NLP problem. Prerequisites: programming abilities (python), linear algebra,
Math 21 or equivalent, machine learning background (
CS 229 or similar) Recommended: machine learning (
CS 229,
CS 228),
CS 224N,
EE364a (convex optimization),
CS 231N

Instructors:
Socher, R. (PI)

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

Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as
EE263, basic probability.

Terms: Win, Sum
| Units: 3

Instructors:
Boyd, S. (PI)
;
Nasiri Mahalati, R. (PI)

## EE 464: Semidefinite Optimization and Algebraic Techniques

This course focuses on recent developments in optimization,nspecifically on the use of convex optimization to addressnproblems involving polynomial equations and inequalities. Thencourse covers approaches for finding both exact and approximatensolutions to such problems. We will discuss the use of dualitynand algebraic methods to find feasible points and certificates ofninfeasibility, and the solution of polynomial optimizationnproblems using semidefinite programming. The course coversntheoretical foundations as well as algorithms and theirncomplexity. Prerequisites: EE364A or equivalent course on convexnoptimization.

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