LLAW6194/JDOC6194 Competition Law in the Digital EconomyR

Java Python Competition Law in the Digital Economy

Course Schedule

Competition Law in the Digital Economy

LLAW6194 (LLM Programme), 9 Credits

JDOC6194 (JD Programme), 6 Credits

I. Course Description

With the rise of data-driven markets, competition around privacy has become a main concern for individuals and regulatory organizations. Of similar concern is the ability of dominant actors to favour their own content and to steer and redirect parts of the customer’s journey on the internet. Meanwhile, decisions taken by consumers are increasingly made passively through implicit or explicit product matching and personalized recommendations rather than through active choice. New technologies recommend and purchase products based on spoken, written or inferred requests obtained from users of digital platforms or devices such as mobile phones, speakers and smart assistants.

This course focuses on distinct principles and case law (e.g., Facebook, Google, Apple, Amazon) pertaining to competition in data-driven markets. This includes: the elusive problem of how quality, rather than price, competition works; how consumers can navigate data-driven markets when conventional market mechanisms are no longer the main disciplining forces on the behaviour of dominant actors; and the conditions under which different regulatory instruments such as ex ante and/or ex post legal interventions –  including market  studies and market investigations  –  can effectively address the predicaments of data-driven markets.

Students will acquire an in-depth understanding of EU competition law relating to digital markets (social media, search, app stores,  online marketplaces) and will be able to compare and assess these developments in light of emerging litigation in the US.

II. Course Organization

Course Materials:

The basis for course discussions is the material contained in the following course programme (III). The materials to be studied for each lecture will be published on Moodle approximately one week before the beginning of each class. Relevant regulations are the European Union competition law rules. Further information will be announced on Moodle and in class. All participants will be expected to read the case materials prior to each class (available on Moodle) and undertake (if necessary) any additional reading specifically required for the pre-assigned topic.

Final Take Home Exam: can do the exam at home there’s time limit for finishing exam (24hr or 48hr for example)

Case Commentary: 1000-1500 words (excluding footnotes, etc.) —> Choose one cases listed from the course program as mandatory reading

• Must choose two topic preferences by September 15, 2024 11:59pm

• Email with name, first name and student number

• Due by November 30, 2024, 11:59pm (Send them via email in Word format through Moodle).

LLAW6194/JDOC6194 Competition Law in the Digital EconomyR

Final Take Home Exam: 2500 words (excluding footnotes, bibliography, etc.)

• Topics are done by the Prof.

Content of the case commentary:

• Summary of procedural history and facts only to extent necessary

• Main focus is on critical engagement with the case and the assigned topic

• Incisive exploration of the relevant literature as well as theoretical and practical reflection on the given topic are indispensable.

• Cannot be a summary of the case

• Use same quotation format consistantly

Assessment(s):

Assessment tasks consist of 1) a final take home exam on a given topic (max. 2500 words, excluding footnotes and bibliography), which deals with the relevant literature and critically reflects upon the subjects listed in the course program and the overall context and cases covered in the course (50%), 2) a written assessment in the form. of a commentary on a case listed in the course program (max. 1500 words, excluding footnotes and bibliography), which puts the case in context with the discussed topic and/or similar cases from European and/or U.S. practice (30%), and 3) class attendance and participation during the course, especially in the context of discussing the assigned cases (20%).

Case Selection and Deadlines:

Students inform. the Professor of their interest in two cases from the course program after the first session. Register via email with a) your surname, first name and HKU student number and b) two topic preferences - the final assignment of cases is done by the Professor. Written case commentaries on the assigned cases are due on November 30, 2024, 11:59 pm (submission in Word format). The final take-home exam must be completed individually. The exam will be distributed and submitted via Moodle. Late submissions are subject to an immediate deduction of 3% of the full mark of the coursework for failing to submit by the required completion date and time and the deduction of a further 3% of the full mark of the coursework for every additional day late thereafter.

Evaluation:

Assessments will be graded based on quality of legal analysis, written expression and demonstrated familiarity with course materials, class topics, participation and subject-matter. For case commentaries, the following rules apply: procedural history and facts of the case should be summarized only to the extent necessary. The main focus of the assignment should consist in a critical engagement with the case in the context of the assigned topic. For take home examinations, incisive exploration of the relevant literature as well as theoretical and practical reflection on the given topic are indispensable. As regards formalities, general citation rules apply. A separate cover sheet with personal details, a complete bibliography and a personally signed declaration of original work must be attached to each written assignment.

General rules:

The Law Faculty’s rules prohibiting cheating, plagiarism and taking unfair advantage apply strictly to written assignments and final take-home exams. All written work will be run through plagiarism detection  software as part of the submission process. Regular class attendance and class participation is expected. This standard is not met in case of conspicuous and regular absence or through inadequate class preparation. The structure of this course encourages and requires participation by students         

深度学习作为人工智能的关键分支,依托多层神经网络架构对高维数据进行模式识别与函数逼近,广泛应用于连续变量预测任务。在Python编程环境中,得益于TensorFlow、PyTorch等框架的成熟生态,研究者能够高效构建面向回归分析的神经网络模型。本资源库聚焦于通过循环神经网络及其优化变体解决时序预测问题,特别针对传统RNN在长程依赖建模中的梯度异常现象,引入具有门控机制的长短期记忆网络(LSTM)以增强序列建模能力。 实践案例涵盖从数据预处理到模型评估的全流程:首先对原始时序数据进行标准化处理与滑动窗口分割,随后构建包含嵌入层、双向LSTM层及全连接层的网络结构。在模型训练阶段,采用自适应矩估计优化器配合早停策略,通过损失函数曲线监测过拟合现象。性能评估不仅关注均方根误差等量化指标,还通过预测值与真实值的轨迹可视化进行定性分析。 资源包内部分为三个核心模块:其一是经过清洗的金融时序数据集,包含标准化后的股价波动记录;其二是模块化编程实现的模型构建、训练与验证流程;其三是基于Matplotlib实现的动态结果展示系统。所有代码均遵循面向对象设计原则,提供完整的类型注解与异常处理机制。 该实践项目揭示了深度神经网络在非线性回归任务中的优势:通过多层非线性变换,模型能够捕获数据中的高阶相互作用,而Dropout层与正则化技术的运用则保障了泛化能力。值得注意的是,当处理高频时序数据时,需特别注意序列平稳性检验与季节性分解等预处理步骤,这对预测精度具有决定性影响。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
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