New Undergraduate Course Descriptions

The following are new undergraduate courses effective in Fall 2019: 

 

CSE 106. Discrete and Continuous Optimization

4 units

Prerequisites: (MATH18 or MATH31AH) and (MATH20C or MATH31BH) and (CSE21 or DSC40B or MATH154 or MATH184A)

Restrictions: None

One frequently deals with problems in engineering, data science, business, economics, and other disciplines for which algorithmic solutions that optimize a given quantity under constraints are desired. This course is an introduction to the models, theory, methods, and applications of discrete and continuous optimization. Topics include shortest paths, flows, linear, integer and convex programming, and continuous optimization techniques such as steepest descent and Lagrange multipliers.

 

CSE 150A. Introduction to Artificial Intelligence: Probabilistic Reasoning and Decision-Making (Pending approval for Winter 2020)

4 units

Prerequisites: (CSE12 or DSC40B) and (CSE15L or DSC80) and (CSE103 or ECE109 or ECON120A or MATH180A or MATH183) and (MATH20A) and (MATH18 or MATH31AH)

Restrictions: Restricted to students with sophomore, junior or senior standing within the CS25, CS26, CS27, CS28, EC26, and DS25 majors. All other students will be allowed as space permits.

Introduction to probabilistic models at the heart of modern artificial intelligence. Specific topics to be covered include: probabilistic methods for reasoning and decision-making under uncertainty; inference and learning in Bayesian networks; prediction and planning in Markov decision processes; applications to intelligent systems, speech and natural language processing, information retrieval, and robotics.

Note: Do not take CSE 150A if you took CSE 150 with the same instructor (i.e. if you took CSE 150 with Professor Alvarado, do not take CSE 150A with Professor Alvarado. It will be the same course and credit may not be received for the same course.) This course may count towards the Learning, Visual, Graphics requirements for CS26 majors. Students will need to contact an advisor via the Virtual Advising Center (VAC: vac.ucsd.edu) to request to update their degree audit.

 

CSE 150B. Introduction to Artificial Intelligence: Search and Reasoning (Pending approval for Winter 2020)

4 units

Prerequisites: (CSE12 or DSC40B) and (CSE15L or DSC80) and (CSE103 or ECE109 or ECON120A or MATH180A or MATH183) and (CSE100)

Restrictions: Restricted to students with sophomore, junior or senior standing within the CS25, CS26, CS27, CS28, EC26, and DS25 majors. All other students will be allowed as space permits.

The course will introduce important ideas and algorithms in search and reasoning, and demonstrate how they are used in practical AI applications. Topics include: A* Search, Adversarial Search, Monte Carlo Tree Search, Reinforcement Learning, Constraint Solving and Optimization, Propositional and First-order Reasoning.

Note: Do not take CSE 150B if you took CSE 150 with the same instructor (i.e. if you took CSE 150 with Professor Gao, do not take CSE 150B with Professor Gao. It will be the same course and credit may not be received for the same course.) This course may count towards the Learning, Visual, Graphics requirements for CS26 majors. Students will need to contact an advisor via the Virtual Advising Center (VAC: vac.ucsd.edu) to request to update their degree audit.

 

CSE 151A. Introduction to Machine Learning (Pending approval for Winter 2020)

4 units

Prerequisites: (CSE12 or DSC40B) and (CSE15L or DSC80) and (CSE103 or ECE109 or ECON120A or MATH183) and (MATH18 or MATH31AH)

Restrictions: Restricted to students with sophomore, junior or senior standing within the CS25, CS26, CS27, CS28, EC26, and DS25 majors. All other students will be allowed as space permits.

Broad introduction to machine-learning. The topics include some topics in supervised learning, such as k-nearest neighbor classifiers, decision trees, boosting and perceptrons, and topics in unsupervised learning, such as k-means, and hierarchical clustering.  In addition to the actual algorithms, the course focuses on the principles behind the algorithms.

Note: Students will not receive credit for both CSE 151A and COGS 188. Students who have previously completed CSE 151, may not receive credit for CSE 151A (therefore do NOT enroll in this course). This course may count towards the Learning, Visual, Graphics requirements for CS26 majors. Students will need to contact an advisor via the Virtual Advising Center (VAC: vac.ucsd.edu) to request to update their degree audit.

 

CSE 154. Deep Learning (Pending approval to be renumbered to CSE 151B after Winter 2020)

4 units

Prerequisites: (MATH20C or MATH31BH)

Restrictions: None

This course covers the fundamentals of neural networks: we introduce Linear regression, Logistic regression, Perceptrons, multilayer networks and back-propagation, convolutional neural networks, recurrent networks, and deep networks trained by reinforcement learning.

Note: Students who previously took CSE 190 Neural Networks with Professor Cottrell will NOT receive credit for this course because it is the same course. If you choose to enroll in the course even though you completed CSE 190 Neural Networks, you will be issued an F. Either one of these courses, CSE 190 Neural Networks or CSE 154 Deep Learning, may count towards the Learning, Visual, Graphics requirements for CS26 majors. Students will need to contact an advisor via the Virtual Advising Center (VAC: vac.ucsd.edu) to request to update their degree audit.